スカイロジック List of Products and Services

EasyMonitoring2 automatically reads and records meter data simply by installing a network camera in front of the meter that needs monitoring, without requiring any additional actions. It eliminates the cumbersome tasks of visual checks, recording in ledgers, and re-entering data into Excel, thereby streamlining operations. Since it continuously monitors with a network camera, it eliminates the need for patrols and can obtain data as frequently as every few seconds to determine if there are any abnormalities. To eliminate the need for patrols, EasyMonitoring2 not only has a meter reading function but also possesses various features to identify valve positions, water leaks, turbidity, and other conditions typically checked visually. **Advantages of EasyMonitoring2** - Easy and quick implementation - A system configuration that is self-contained within the facility - Compatible with various network cameras - No operational costs due to a one-time purchase package - Supports condition checks for flames, liquids, and more - Utilizes deep learning (AI) *For more details, please refer to the PDF materials or feel free to contact us.*

This is a verification request from a parts manufacturer. Our company has various industrial machinery and control equipment manufacturers as clients in Hamamatsu City, Shizuoka Prefecture, where the manufacturing industry is thriving. We have a track record of providing support for over 1,000 cases and have developed inspection software that is easy to integrate and sold as a one-time purchase, which we believe leads to repeat business. 【Inspection Settings and Results】 The cell count detection in the left image is the result of counting cells using EasyInspector (traditional rule-based method). It compares how many clusters of the same color can be detected within a fixed-size inspection frame. 【Software Used】 Software used: EasyInspector (formerly EasyInspector) The current 'EasyInspector2' color package can perform inspections based on the presence or absence of specified colors.

We will introduce a case where we used the reflective material "the back side of a CD" as a mirror-like workpiece to detect objects with a simple setup. By using a glossy workpiece that seems difficult to inspect at first glance, we created a simple inspection setup to ensure that "defective products do not flow further" by continuously inspecting the conveyor with a fixed-point camera. The applications are diverse, and it can surely be utilized in your industry as well. If you have been giving up on inspecting workpieces that cause halation or painted objects, please contact us. 【Software and Equipment Used (Excerpt)】 ■ Software Used: DeepSky (Learning Version) ■ Field of View: 160×140mm ■ Minimum Size of Inspection Target: 1mm ■ Number of Inspection Points: 1 ■ Camera Resolution: 1.3 Megapixels MER-133-54U3M (Daheng) *For more details, please refer to the PDF document or feel free to contact us.

This time, we are inspecting the top surface and the entire circumference of the cap using only one camera (without using multiple cameras). We will introduce a case where the friction force is utilized to rotate the cap. When the AI image inspection software DeepSky detects a defect, an NG signal is output from the Intelligent I/O, and by connecting an electromagnetic valve to the NG output, the valve opens when a defect is detected. At this time, air from the air compressor flows to the pusher, causing the pusher to extend forward and automatically discharge the defective product. For more details, please check the related links below. 【Tools and Equipment (Excerpt)】 ■PC: Mouse Computer/G-Tune, Core i9, 16GB RAM, RTX 2070 SUPER ■Image Processing Software: Sky Logic/DeepSky DS100K ■IO Unit: Sky Logic/EI-ITIO-T01 ■1.3 Megapixel Camera: Daheng/MER-133-54u3c ■12mm Lens: M1214-MP2 *For more details, please refer to the PDF document or feel free to contact us.

This is a request from a manufacturer that produces plastic molded products. They have a cap with a diameter of approximately 30mm and a height of about 8mm, but sometimes black spots (approximately 0.2mm in diameter) appear on the flat top. They would like to conduct an appearance inspection of this cap and inquired whether it is possible to detect these black spots through their website. We conducted verification using the inspection software "DeepSky" (which utilizes AI and deep learning) and "EasyInspector" (a traditional rule-based inspection software). In this case, it can be said that verifying with 'DeepSky' is smoother. This software can be easily operated in a manner similar to operating Windows software, which leads to many direct inquiries from end-users who are introducing image inspection for the first time. [Software Used] ■DeepSky ■EasyInspector (formerly EasyInspector) *For more details, please refer to the PDF document or feel free to contact us.

This is a request for verification of painted parts used in the interiors of luxury cars. The increase in inquiries about "paint defects" is one example of this trend. Various paint manufacturers have adopted our image inspection technology. We offer a "simple evaluation service" free of charge, provided by our technical staff. If defects or judgments can be detected during the simple evaluation, we recommend conducting a "feasibility verification" test that simulates actual operations to evaluate processing time and judgment accuracy. Additionally, we can lend demo units free of charge, allowing users to experience the deep learning inspection software firsthand. By using DeepSky's inspection capabilities, inspections were possible. The workpieces to be inspected are conveyed on a conveyor, and one side is magnified for imaging. Adjusting the lighting when imaging painted products is a key point.

We received a message from a trading company saying, 'We are looking for a system that can perform image inspection.' They were considering integrating it into an automatic assembly machine, receiving signals from a higher-level control device, conducting imaging and judgment, and returning the judgment results to the higher-level control device. Many trading companies and industrial equipment manufacturers repeatedly purchase our inspection software because it is easy to integrate and sold as a one-time purchase. As a result of testing with the sample products we have, we found that it is possible to detect the bias of the sponge and the presence of rubber using EasyInspector's 'Presence of Specified Color Inspection.' For the sponge bias, the setting is that if a color other than the sponge is detected, it is considered a failure. For the presence of rubber, black is designated as the specified color within the inspection frame, and if rubber is present, detecting black results in a pass. The light blue fluorescent pixels in the image indicate the detected specified color.

Rubber molded parts, such as automotive components, have different clip mounting positions depending on the product. This time, we received a request for a simple verification of clip position measurement. If the field of view is approximately 250mm in the longitudinal direction, it seems possible to measure the clip pitch with an accuracy of ±0.5mm. By using the "Dimension and Angle Inspection" feature of EasyInspector, we were able to detect the differences in the positions of four clips and determine visually similar similar products (different items) in less than 0.93 seconds. The image on the right shows the detection frame. Our company accepts verification and support from technical staff on a daily basis. If you have any issues, questions, or uncertainties during operation, please feel free to contact us at any time. 【Software Used】 EasyInspector710 (formerly EasyInspector) The current 'EasyInspector2' MS (MeaSure) package can be used for position and width measurement inspections.

We will conduct a simple inspection of foreign substances and insects on a colorless transparent PP sheet measuring 650×800. Before the inquiry, visual inspections were performed by workers stacking the sheets, but there were concerns about the possibility of foreign substances and insects being mixed in, so we received a request to prevent this. 【Inspection Setup and Results】 We affixed 10 defect seals measuring 0.3mm square to the good sample sheets we received and verified whether all 10 black dots could be detected. By using the "Scratch and Defect Inspection" feature of EasyInspector, we were able to inspect the entire area in 1.84 seconds. The left image is a live magnified view, and the right image is a magnified view of the inspection results. The non-compliant areas are displayed in red, indicating a perimeter detection of 11 pixels. 【Software and Equipment Used】 Software Used: EasyInspector (formerly EasyInspector) Field of View: Approximately 800 x 500mm Minimum Size of Inspection Target: 2mm Number of Inspection Points: 1 Camera Resolution: 20 million pixels Lens Focal Length: 16mm Distance Between Lens and Product: Approximately 980mm Lighting: Indoor fluorescent lights The current 'EasyInspector2' color package can be used for [Scratch and Defect Detection] inspections.

There was a request to measure the red frame area of the magic using heat-shrinkable transparent film. The manufacturer of chemical products and plastic processing for this simple verification is a repeat customer who has been inquiring for some time. 【Inspection Settings and Results】 In the image example (Figure 2), the lengths from each opposing frame to the frame are being measured. Since the number of frames can be increased up to 999, measuring arbitrary points vertically and horizontally and calculating the average value should improve accuracy compared to measuring only four points. The measurement values are calculated based on pixel counts, and by multiplying this by a coefficient that indicates how many millimeters one pixel represents, the actual dimensions can be converted.

A customer, a high-performance plastic resin manufacturer, contacted us through our website regarding their concerns about the user manual inspection. They mentioned that due to the large amount of text and small font size, they are experiencing missed inspections and longer inspection times. 【Inspection Settings and Results】 By using the "Comparison with Master Image" feature of EasyInspector, we were able to cover 32 inspection items for the labels and make determinations. The inspection time during verification was approximately 15 seconds, but when we set up an overall inspection frame and performed more detailed "shift correction," it took about 2 minutes. 【Software and Equipment Used】 Software Used: EasyInspector (formerly EasyInspector) Field of View: Approximately 622 x 455mm Minimum Size of Inspection Target: 2mm Number of Inspection Points: 8 Camera Resolution: 14 million pixels Lens Focal Length: 12mm Distance from Lens to Product: Approximately 540mm Lighting: Two bar lights Distance from Lighting to Inspection Item: Illuminated from about 100mm above the left and right sides The current 'EasyInspector2' color package can perform inspections using the "Comparison with Master Image" feature.

The plastic processing manufacturer has been a customer using our inspection software for some time. This simple verification is in response to their request to detect "chipping" and "bubbles" in plastic products. 【Inspection Settings and Results】 By using the "Scratch and Defect Inspection" feature of EasyInspector, we were able to inspect one location in 0.41 seconds. The images show the inspection results. The frame in the left image indicates an OK item, as no abnormalities such as chipping were detected, resulting in a "pass" judgment marked in blue. The right image shows a defective item, where the chipping was detected, leading to a "fail" judgment displayed in a red frame. 【Software and Equipment Used】 Software Used: EasyInspector (formerly EasyInspector) Field of View: Approximately 299 x 238 mm Minimum Size of Inspection Target: 10 mm Number of Inspection Points: 1 Camera Resolution: 1.3 Megapixels Lens Focal Length: 8 mm Distance Between Lens and Product: Approximately 360 mm Lighting: Bar Lighting Distance from Lighting to Inspection Item: Illuminated from 650 mm above Current inspection can be performed with the 'EasyInspector2' color package for [Scratch and Defect Detection].

We will conduct a simple inspection of the printed part of the container. We received samples from a plastic processing manufacturer. In recent years, the decline in the working population and the aging population have become social issues. Amidst this, the need for automation in inspection and monitoring to maintain a safe and secure society is increasing daily. 【Inspection Settings and Results】 By using the "Comparison with Master Image" function of EasyInspector, it was possible to determine the pass/fail status of the printed parts in 0.32 seconds. The left image shows the master screen with alignment correction set in light blue and red frames (it is common to set this with a good product image), while the right image indicates that a defect was detected within the red frame. 【Software and Equipment Used】 Software Used: EasyInspector (formerly EasyInspector) Field of View: No recording Minimum Size of Inspection Target: 2mm Number of Inspection Points: 8 Camera Resolution: 300,000 pixels Lens Focal Length: 6mm Distance Between Lens and Product: Approximately 470mm Lighting: Fluorescent bar lighting Distance Between Lighting and Inspection Item: Illuminated from inside the workpiece The current 'EasyInspector2' color package can be used for inspection with the "Comparison with Master Image" feature.

A housing material manufacturer is considering our inspection software for product management of precision plastic extrusions, such as automotive parts, and has made an inquiry. 【Inspection Settings and Results】 By using the "Dimension Angle Inspection" feature of EasyInspector, we were able to measure 22 points in 0.53 seconds. For a 5-megapixel camera (with a horizontal field of view of approximately 80mm): Since the horizontal resolution is 2592 pixels, the size of 1 pixel is "80÷2592=0.0308…mm." This results in a rough inspection with increments of about 0.0308mm, but as mentioned above, judgments were possible. For a 20-megapixel camera (with a horizontal field of view of approximately 80mm): Since the horizontal resolution is 5496 pixels, the size of 1 pixel is "80÷5496=0.0145…mm." This allows for detection with double the precision compared to the 5-megapixel camera with increments of about 0.0145mm, but even with 20 megapixels, the inspection remains rough.

We received samples from a manufacturer of plastic products. We conducted a simple verification to check for missing screws, crankshaft pins, missing sheet metal, and the harness route. As a result of the verification conducted with the samples we received, we were able to determine using the EasyInspector "Comparison with Master Image" function. Since the housing is transparent, we took the photos against a black background. The fluorescent light was illuminated from the side to avoid reflections. The system is designed to allow inspection of both the front and back surfaces by setting up two or more multi-cameras. 【Software and Equipment Used】 Software Used: EasyInspector710 (formerly EasyInspector) Field of View: Approximately 172 x 131 mm Minimum Size of Inspection Target: 5 mm Number of Inspection Points: 4 Camera Resolution: 2 million pixels Lens Focal Length: 12 mm Distance Between Lens and Product: Approximately 300 mm Lighting: Linear fluorescent light Distance Between Lighting and Inspection Item: Illuminated from the side of the workpiece The current 'EasyInspector2' color package can be used for inspection with the "Comparison with Master Image" feature.

Even manufacturers of high-precision resin-molded parts, such as precision components, are considering our inspection software. In preliminary simple verifications before understanding the operational situation and requirements, we may report on both conventional rule-based inspection software and AI (deep learning) software to determine which better meets their needs. 【Inspection Settings and Results】 In the enlarged image of the detection by EasyInspector on the left, the field of view was set to accommodate the workpiece, and when inspecting defective samples, it was possible to detect black spots and dirt. However, if there are irregularities in shape or shadows of contours within the inspection area, there is a possibility of misdetection. Conventional software is more susceptible to positional deviations. The image on the right shows the case using DeepSky. Through deep learning, it learns defects and detects only the defective areas from the image. Due to its nature, if properly trained, it can detect the intended defective areas even in the presence of variations caused by pad adhesion. 【Software Used】 Software used: EasyInspector, DeepSky *The current EasyInspector2 also includes AI functionality.

This is a request for a simple verification using images captured with a microscope. We will measure the diameter of the cross-section of a 24G tube (outer diameter 0.70mm) using an image taken at a resolution of 4000×3000px. 【Inspection Settings and Results】 By using the "Dimension Angle Inspection" feature of EasyInspector, we were able to measure the diameter of the cross-section of one location on the tube. The measurement taken from the image captured with the microscope showed a diameter (indicated by the red arrow) of 2369px. Since we do not know the actual dimensions of this product, substituting the nominal size gives us 0.7mm = 2369px, resulting in 1px = 0.0003mm, which is the resolution. Generally, the measurement error is ten times the resolution, so that value is 0.003mm. Under these conditions, inspection is possible, but to measure the outer diameter, the entire inspection item must be within the field of view. As the outer diameter of the inspection item increases, the actual dimension value per pixel also increases, leading to a larger measurement error.

This is an inquiry from a manufacturer of plastic products processing. Even in the case of small-batch production of various types, our image inspection can assist with operational efficiency. Our company accepts verification and support from technical staff on a daily basis. If you have any issues, questions, or uncertainties during operation, we would be grateful if you could feel free to contact us at any time. 【Inspection Settings and Results】 By using the "Comparison with Master Image" function of EasyInspector, we were able to detect defective areas within four inspection frames. By dividing the inspection frame into four parts from the entire screen, it becomes possible to correct shifts in each inspection frame. As a result, we can suppress noise (very small differences) that may detect even good parts.

In a comprehensive assembly manufacturer specializing in contract design and production of resin molded products, which has been using our inspection software for some time, an incident occurred where the company logo printing was incomplete. As a result, we provided guidance on the setup method and conducted verification. We received photos of acceptable products and created images with defects using Illustrator. 【Inspection Settings and Results】 We will conduct inspections using EasyInspector's "Comparison Inspection with Master Image." The inspection target area is recognized to be about the size of the images you sent (approximately 50mm wide), and we believe that a camera with around 5 million pixels should be able to detect within this field of view (it is necessary to set up the inspection environment with appropriate lighting, etc.). When the printing is incomplete, the white background becomes visible, so if we can capture a good contrast between black and white with lighting, we should be able to detect the defects. If you could send us images of acceptable and defective products with the camera and work (fixed), we can also conduct verification on our side.

The manufacturer of the autonomous driving assistance system has considered our character recognition function for product management. The products display multiple characters and logos. Inspections for missing or faded displays and for recording purposes are made easier with the recognition function. This is also a common case, and we have received applications from various manufacturers. [Inspection Settings and Results] As a result of verification conducted with the sample we received, it was determined using EasyInspector. For the inspection of missing or faded areas, we used the comparison function with the master image, and for character recognition (recording and correctness judgment), we utilized the OCR function. We set up 26 inspection frames, requiring an inspection cycle of 1.95 seconds. The recording method can be done in CSV and image formats.

A plastic processing manufacturer requested a simple verification to count the number of products on a tray. 【Inspection Settings and Results】 As a result of the verification with samples, the number of products on the tray could be counted accurately. However, if there is extreme unevenness in the way light hits the products, false detections are likely to occur. While counting was possible, it seemed necessary to adjust the lighting environment so that the entire setup appears to have a similar color tone. (During the verification, adjustments were made under indoor lighting to the exposure time and the position of the inspection frame to avoid false detections.) 【Software and Equipment Used】 Software Used: EasyInspector710 (formerly EasyInspector) Field of View: Approximately 531 x 426 mm Minimum Size of Inspection Target: 15 mm Number of Inspection Points: 1 Camera Resolution: 1.3 Megapixels Lens Focal Length: 8 mm Distance Between Lens and Product: Approximately 700 mm Lighting: Indoor Fluorescent Light

This is an inquiry from the manufacturer of air pumps. This time, we will check the possibility of missing paint removal. Our company, SkyLogic, has a track record of over 2,000 image inspections. We have published numerous inspection cases for metals, plastics, food, electronic substrates, pharmaceuticals, etc., so please check our case search tool to see if there are similar cases to the inspection you are considering. 【Inspection Settings and Results】 We conducted verification using the samples you sent. We were able to distinguish between the painted areas and the removed areas, so we believe that the inspection for missed removal is feasible. We also think it is possible to inspect the sides simultaneously and confirm the presence or absence of marks and labels. However, in both cases, it will involve inspecting multiple surfaces, and positional shifts may affect the inspection, so we believe it is necessary to fix the position of the workpiece using jigs or similar tools. (If the inspection is only for one surface, the shift correction function can probably be used.) The left image shows the imaging environment. The right image shows the settings.

We conducted a free evaluation of the appearance inspection of resin molded products. This involved detecting foreign objects, burrs, and shorts in a circular part with a diameter of 35mm. The ideal inspection setup was to observe the parts on a conveyor without fixing their position, with a target inspection cycle of 1 to 2 seconds. This inquiry is a case that falls within the expertise of our appearance inspection software, DeepSky. 【Inspection Settings and Results】 Regarding the detection of "foreign objects, burrs, and shorts" in the resin molded products you sent, we were able to detect foreign objects and burrs with high accuracy. For the foreign objects in the samples you provided, we achieved nearly 100% detection. For the burrs in the samples you provided, we also achieved nearly 100% detection. For shorts (missing pieces), we achieved nearly 100% detection in the samples you provided. However, for shorts (small chips), there were instances where detection was not possible, with an accuracy of about 20-30%. This time, we used software called DeepSky, which employs AI (Deep Learning) for inspection. By training the software on the areas we want to detect, it adjusts its own settings and improves its recognition capabilities.

We will inspect the cushioning foam attached to the Styrofoam used for packaging the product. This inquiry comes from a manufacturer of woodworking and plastic processing machinery. They consulted us regarding the inspection of the presence or absence of gray and black cushioning foam attached to the front and back surfaces, stating that positional accuracy is not currently necessary. They would like to determine whether the foam is partially present or absent and whether it is applied overall. 【Inspection Setup and Results】 We conducted the inspection of the presence or absence of the cushioning foam using the sample images you provided. By using EasyInspector's "Presence of Specified Color Inspection," we were able to determine the presence of the cushioning foam. Additionally, we could also determine the presence of plastic parts using the same function. Since you provided images of the parts installed (front and back), we created test images in a paint software showing a state where some parts are missing (NG state) for verification. The left image shows the inspection frame, and the right image shows the detection frame.

This is a request for image assessment to check for holes in a circular rubber flange. It is noted that defective products either have no holes (clear points) or have very few holes, or the holes are small. Additionally, there are concerns about detecting burrs, defects on the outer circumference, and debris (clear points outside the hole positions). A sample has been sent from a company involved in ground investigation and ground improvement. 【Inspection Settings and Results】 We conducted verification of hole presence and position inspection using the sample provided. As a result, we were able to detect the hole areas and determine their positions and presence. However, if the position of the holes is also to be inspected, the following conditions are necessary: the inspection item should be fixed in place using an L-shaped fixture or similar. The orientation of the inspection item should also be set to be approximately the same. Regarding the orientation of the inspection item, please align it roughly based on the areas where numbers or letters are displayed on the surface. Using the "Presence of Specified Color Inspection" function of EasyInspector, we were able to detect the presence or absence of holes and differences at five locations, and we could determine visually similar similar products (different items) in 0.59 seconds.

We received an inquiry from a manufacturer specializing in automatic control design and maintenance. They were considering conducting visual inspections for container lids made from injection-molded products, which are placed on an index table and processed at a rate of 18 pieces per minute (3 seconds per piece). They attempted to detect "scratches," "welds," and "spot debris." 【Inspection Settings and Results】 Initially, we conducted verification using images captured within the illuminated area. In the verification environment, it was possible to identify scratches, welds, and spot debris. Additionally, for weld lines specifically, an imaging method using a polarizing filter (as shown in the left diagram) proved effective. Our company offers verification and support from technical staff on a daily basis. If you have any issues, questions, or uncertainties during operation, please feel free to contact us at any time. 【Software Used】 Software Used: DeepSky

There have been instances where defects such as scratches, bumps, and foreign matter contamination occur in resin-molded parts like automotive components due to processing methods, causing difficulties. 【Inspection Settings and Results】 By using the "Scratch and Bump Inspection" feature of EasyInspector, we were able to determine scratches and bumps across the entire field of view in 0.5 seconds. Two inspection frames were created for the entire area, setting them apart as "white" and "black." 【Software and Equipment Used】 Software Used: EasyInspector710 Field of View: 10 x 6 mm Minimum Size of Inspection Target: 2 mm Number of Inspection Points: 1 (entire area) Camera Resolution: 300,000 pixels Lens Focal Length: 50 mm + 10 mm Macro Ring Distance Between Lens and Product: 190 mm Lighting: Ring Lighting Distance from Lighting to Inspection Item: Not recorded The current 'EasyInspector2' color package can be used for [Scratch and Bump Detection].

The samples you sent this time included a clear significant short and a subtle short that is not easily noticeable at first glance. It was confirmed that even minor shorts exhibited surface roughness associated with the short. With the inspection using EasyInspector, it would be easier to report if you could send us a "master image (good product)" and a "defective product that seems the most difficult to detect." 【Inspection Settings and Results】 Using EasyInspector's "color comparison inspection - comparison with master image" inspection function, we were able to conduct the inspection in 0.45 seconds. Even subtle shorts that were close in shape to the master image were detected for surface roughness (reflected white) and judged as unacceptable. The arrow markings in the photo are also white, but this time they were excluded from detection due to the non-detection pixel settings.

We received an inquiry from a manufacturer that produces a wide range of products, including vinyl chloride monomers, polymers, and special PVC resins, with whom we have had a business relationship for some time. They asked if our group company could distinguish the shapes of molded products (specifically, symmetrical shapes) and contacted us regarding this matter. 【Inspection Settings and Results】 We used a total of 14 images as training data, consisting of 7 images of the left side and 7 images of the right side. We enclosed the object we wanted to detect (in this case, the entire workpiece) in a frame and labeled them as "LH" and "RH" respectively (as shown in the left image). This process was carried out for all 14 training data images. In the right image, LH is correctly recognized as LH, and RH is recognized as RH. There are no issues with recognition even if the left and right are swapped. 【Software and Equipment Used】 Software Used: DeepSky Field of View: Approximately 482 x 383 mm Minimum Size of Inspection Target: 150 mm Number of Inspection Points: 1 (to check if the left and right workpieces are aligned across the entire screen) Camera Resolution: 1.3 million pixels Lens Focal Length: 8 mm Distance Between Lens and Product: Approximately 575 mm Lighting: Indoor fluorescent lights

Inquiry regarding scratch assessment for 200×100 mm square orange lens (plastic lens). There are requests for scratch assessment of plastic lenses, assessment of plastic molded product dents (depressions), assessment of plastic laser printing, and other appearance inspection requirements. In the initial stage, a free simple verification will be conducted, followed by guidance for paid verification. 【Inspection Settings and Results】 We verified whether scratches could be detected using the image inspection software EasyInspector. As a result, detection was possible, but false detections occurred in other areas depending on the type of scratch, making it impossible to detect "only defective scratches." With our product, the AI image inspection software "DeepSky," which uses AI (deep learning) capabilities, the AI can automatically adjust the setting parameters and recognize only the scratches as the target object. Therefore, it is recommended as it is less affected by changes in appearance due to lighting variations. 【Software Used】 Software used: EasyInspector710 Currently, EasyInspector2 also has AI capabilities and supports various inspections. We will propose solutions tailored to the inspection target.

One of the frequently asked points from end users who are introducing image inspection for the first time is the ability to operate it in a manner similar to Windows software. This year, we have also developed related software that is useful for actual operations, such as the highly requested "simultaneous inspection and counting on a conveyor" and "OCR for difficult-to-read engravings." Additionally, we can add an optional "extended command" that enables a wide range of system operations. [Inspection Settings and Inspection Results] The image involves a task called "annotation," where the area to be detected is enclosed in a frame. By "training" the enclosed area, we were able to detect the target. Furthermore, as an image of actual operation, for example, when an abnormality is found while continuously photographing moving products, actions such as lighting a lamp, sounding a buzzer, or stopping the conveyor can be performed.

This is an inquiry from a manufacturer of thermosetting and thermoplastic resin molding processing. The inspection is based on the images provided. 【Inspection Settings and Results】 The left image shows the annotation work to outline the area to be inspected. The right image shows the detection frame. There were no instances where defective products were incorrectly classified as acceptable, but there were 2 images where acceptable products were incorrectly classified as defective. Since September 2021, a data augmentation feature for training images has been implemented. By using this convenient feature, it is possible to learn from a larger number of images with variations in orientation, angle, and brightness of defects that occur only occasionally, which improves accuracy. 【Software Used】 Software Used: DeepSky Learning Version Number of Inspection Points: 4

The interior of vehicles, such as cars, can be customized in terms of color and decoration according to the grade. We received an inquiry regarding color matching from an industrial machinery manufacturer with whom we have had previous dealings. They sent us sample images, and we conducted a simple verification. Our company website offers a free trial of our inspection software online. You can also experience our inspection software that uses AI (deep learning) firsthand. 【Inspection Settings and Results】 Out of 45 images, 15 (5 each) were used as training images. We inspected a total of 45 images, including the 15 training images and 30 untrained images. The result was that all 45 images were correctly detected. The breakdown of the images is 11 silver, 19 red, and 15 white. 【Software Used】 Software used: DeepSky

This is an inquiry from a manufacturer of automotive parts and accessories. I believe that clearly printing details such as model numbers and manufacturing dates on delivered products is a common practice across various industries. Our software includes basic functions for saving inspection results in CSV format or as images. It allows for the recording of which parts are associated with which product numbers, making it useful in various industries. 【Inspection Settings and Results】 By using the "OCRPro" feature of EasyInspector, we were able to read characters in 26 locations. The reading took 1.87 seconds. For characters, detection was possible using either the OCR function or the OCR Pro function, while for marks and logos, comparison inspection with master images was utilized. The images show differences in RGB and color tones in the "comparison with master image" settings. As for recording methods, results can be saved in CSV format or as images. Information like that in Table 2 will be recorded in CSV format.

At a plastic parts manufacturer, counting small products for shipping has become a burden. There were issues with errors in weight inspection. We will conduct a simple verification of counting small parts. With DeepSky, you can use advanced software that supports counting on a conveyor. It is designed to count in various operational methods. 【Inspection Settings and Results】 We conducted inspections using software called DeepSky, which utilizes AI (Deep Learning). By training the software on the areas we want to detect, it adjusts its own setting parameters and learns to recognize them. The inspection cycle is shorter with EasyInspector. However, counting work that is not placed in trays is difficult to judge, so we decided to verify it with DeepSky this time. The left image shows the annotation, and the right image shows the detection frame. There were misjudgments with work that perfectly overlapped without any gaps, but otherwise, the judgments were good. 【Software Used】 Software used: DeepSky Counting can also be done with the application specialized for counting, 'cazoetell'.

A manufacturer of high-performance precision plastic molds and press molds has contacted us through our website to automate visual inspection. On our website, you can download the 60-day free trial software of EasyInspector and try out DeepSky's web version. Please check it out. 【Inspection Settings and Results】 We submitted the results for both EasyInspector and DeepSky. Considering the variability in the pad suction process and the uncertainty of where defects may appear on the parts, it is likely that DeepSky is more suitable for this situation. The web trial version of DeepSky only allows you to find what you have taught it. If you would like to verify the OK/NG judgment, we can lend you the full version for free. 【Software Used】 Software used: EasyInspector, DeepSky Learning Edition Number of inspection points: 1 location, finding defects from the entire screen.

We often receive requests to count the number of necessary parts while inspecting for any defective items in inquiries to our company. Unmanned inspection and counting on a conveyor belt is a field in which DeepSky excels. This time, we received an inquiry about whether we could count processed parts (e.g., φ0.8×2.5mm) arranged randomly on a flat pallet. They provided images using backlight illumination. 【Inspection Settings and Results】 The left image shows a part of the work screen for the annotation process, where we outline the areas we want to identify (defects). As can be seen in the right image, all 10 annotated teacher images used were counted correctly. The ratio of successful counts for non-teacher images was 30%. There was a tendency for higher rates of miscounting with parts that were more closely stuck together. By using non-detected images for further training, we can detect defects that were previously undetectable. Further training is also one of DeepSky's strengths.

This is an inquiry from a customer who wishes to quantify the texture of fiber-reinforced resin molded products. The preliminary verification is based on the images provided. For accurate verification, sending samples that can also provide guidance on the imaging environment is the most reliable way to ensure stable reporting. However, there are also many requests to send images via email. After determining the pass/fail status through simple inspection, we will proceed with verification while considering the operational methods. 【Inspection Settings and Results】 By using the "Presence of Specified Color Inspection" feature of EasyInspector, we conducted an overall inspection with inspection frames, allowing for quantification. There were patterns resembling cuts, and we determined that EasyInspector provides more stable inspection than DeepSky. 【Software Used】 Software used: EasyInspector710 The current 'EasyInspector2' color package can perform inspections for the "Presence of Specified Color."

We have received many inquiries from industrial equipment manufacturers regarding various case studies. This time, we attempted to set up a test to determine the presence or absence of plastic parts (balls). The cases introduced on this site are for simple verification. We prepare lighting, cameras, lenses, etc., to test whether we can detect the defects we want to see. Typically, we request about two types of samples and detection targets. Since simple verification is a task to confirm the feasibility of detection, quantitative evaluations such as detection accuracy using a large number of samples and the correlation between image resolution and detection accuracy are generally not included. [Inspection Setup and Results] We conducted an inspection of the presence or absence of parts (balls) using the samples you provided. By using ring lighting to make the balls appear black, we were able to determine the presence or absence of the five parts in 0.14 seconds using EasyInspector's "specified color presence check" function.

Automotive parts and other resin-molded components can vary in availability and position due to differences in specifications. However, in the case of similar products, accidents have occurred where similar but incorrect items were shipped because they looked almost identical. We will inspect the presence of clips and rivets. 【Inspection Setup and Results】 By using DeepSky, it was possible to determine the presence of clips and rivets. While it is challenging to make judgments when the items are rotated 90 or 180 degrees, detection was possible by slightly shaking the items in the rotation direction within the visible range and training the system with the shaken images as examples. 【Software and Equipment Used】 Software Used: DeepSky Learning Version Field of View: Approximately 685 x 549 mm Minimum Size of Inspection Target: 2 mm Number of Inspection Points: 6 Camera Resolution: 1.3 Megapixels Lens Focal Length: 8 mm Distance from Lens to Product: Approximately 900 mm Lighting: Indoor Lighting

There have been incidents where resin-molded parts, such as automotive components, were shipped as defective products despite having discoloration or scratches, as they appeared almost identical. In an era where higher quality is demanded across various industries, we recommend automating inspections to ensure stable shipments, especially for defects that are difficult to detect through visual inspection. 【Inspection Settings and Results】 By illuminating at an appropriate angle, we were able to detect and assess the discolored areas. In this case, we set up 4 NG sample parts and captured a total of 12 annotated images by changing the angle. Detection in the environment is only possible for the curved surfaces shown in the images above. When inspecting other surfaces, it is necessary to change the lighting and camera position for imaging. 【Software and Equipment Used】 Software Used: DeepSky Learning Edition Field of View: Approximately 447 x 355 mm Minimum Size of Inspection Target: 2 mm Number of Inspection Points: 1 point covering the entire screen Camera Resolution: 1.3 million pixels Lens Focal Length: 6 mm Distance Between Lens and Product: Approximately 400 mm Lighting: Linear fluorescent lamp Distance Between Lighting and Inspection Item: Approximately 200 mm above

In food manufacturing, crates may be used during delivery. When collecting crates, they may be left in open warehouses or outdoors, which can lead to contamination from bird droppings, paper, and other foreign materials, as well as deformation. This time, we received a request from a customer to ensure that products are received in clean crates, so we examined the dirt and foreign materials attached to the containers. 【Inspection Settings and Results】 For bird droppings, we conducted tests using white, black, and brown paint as shown in the image on the right. After annotating the detection area (the process of enclosing the detection area in a frame) as shown in the left image, we proceeded with "training." We annotated 10 images for each NG variety. The "number of recognition points (standard value)" was set to "10" (the default is "60"), and detection was successful. 【Software and Equipment Used】 Software Used: DeepSky Learning Version Field of View: 400 x 300mm Minimum Size of Inspection Target: 10mm Number of Inspection Points: 1 location to find NG from the entire screen Camera Resolution: 1.3 million pixels Lens Focal Length: 6mm Distance Between Lens and Product: 580mm Lighting: If the brightness varies in the morning and evening near windows, it is recommended to install lighting.

Incidents have occurred where similar resin-molded parts, such as automotive components, were shipped as different products due to their almost identical appearance. We conducted defect detection tests for dents and other issues based on inquiries from trading companies. 【Inspection Settings and Results】 Based on the samples we received, we were able to detect dents. The learning process was executed for approximately 2,400 steps, and the graph converged in about 16 minutes. The time may vary depending on the specifications of the PC used. The main specifications of the PC used for verification are: OS: Windows 10 Home 64bit, CPU: Intel(R) Core(TM) i7-9750H, RAM: 16GB, GPU: NVIDIA GeForce GTX1660 Ti. In this case, defects such as dents are labeled as "anomalies." The left image shows the annotation, and the right image shows the detection frame.

We received an inquiry from a sensing equipment manufacturer regarding burr detection. It seems that as long as we can capture images where the contours of the workpiece are clearly visible, detection itself should be possible, and the key point will be how to create that environment. We have received various consultations regarding burrs from different industries, and many customers have found our inspection software helpful for achieving more stable product shipments. [Inspection Settings and Results] As shown in the image, we are detecting burrs with high accuracy. The teacher images used for this verification consisted of 120 images. This was created by capturing 10 images of good and defective products and rotating each image by 30° for a total of 12 times. As a simple operational method, we reported that the parts are guided from the parts feeder to the backlight conveyor, where they are inspected one by one using a sensor.

Automotive parts and other resin-molded components can vary in availability and position due to differences in specifications. However, in the case of similar products, accidents have occurred where similar but incorrect items were shipped because they looked almost identical. In previous image inspections, it was considered difficult to detect the presence or absence of black clips on black workpieces, as in the current inquiry. 【Inspection Settings and Results】 We conducted inspections using a software called DeepSky, which utilizes AI (Deep Learning). By training the software on the areas we wanted to detect, it adjusts its own setting parameters and learns to recognize them. The left image shows the detection of the installation of a black clip, while the right image correctly determines that the black clip is not installed. 【Software Used】 Software Used: DeepSky Learning Version Number of Inspection Points: 6