スカイロジック List of Products and Services

The manufacturer that produces various sample books has also considered our inspection software. In cases like wood grain patterns, there are often discrepancies in color, texture, and design, making it difficult for traditional rule-based image inspection methods. Our website offers a free web trial of the inspection software. You can experience the inspection software that uses AI (deep learning) and make inquiries based on that experience. [Inspection Settings and Results] The images are annotations. By using software that employs AI (Deep Learning) for inspection and training it on the areas to be detected, the software itself adjusts its setting parameters and grows to recognize them. As a result of testing with the samples you provided, there were some misjudgments, but we achieved 100% detection with the trained images. Since the sample products used as teacher images were only three, there were instances of false detection for "similar colors" and "variations in wood grain." By increasing the number of teacher images, it is possible to learn various patterns of wood grain (features) and improve the accuracy rate. Additionally, capturing images separately from the left and right (with two cameras) and enlarging them is also an effective method.

We received an inquiry from a manufacturer of containers and packaging materials regarding the presence or absence of transparent varnish. Detecting the presence of transparent varnish is difficult with conventional image processing methods like EasyInspector, and if we were to make a suggestion, it would be to use a software called DeepSky that utilizes deep learning. 【Inspection Settings and Results】 Since we only have two images for training data, the results will be for reference only. However, by training the model on images with and without varnish, there is a possibility of distinguishing the presence of varnish as shown in the images. The images you provided were likely taken under stable conditions regarding lighting, positioning, and camera distance. We would like to request an increase in the number of NG and OK images using the same imaging method for further validation. 【Software Used】 Software used: DeepSky

We received sample images of non-woven fabric from various manufacturers of textile products and non-woven fabric. This is a request for a free evaluation to find foreign substances and dirt. Sending actual samples has become stricter recently due to compliance and security concerns. If you can send samples to our company, we can verify and provide guidance on aspects such as lighting, cameras, lenses, and the distance to the work. 【Inspection Settings and Results】 Using the 23 images sent, we were able to detect defective areas. The left image shows the annotation work of enclosing the areas we want to detect with rectangles. The right image shows the detection frames of the inspection results. Out of the 23 images, 16 were used as training images. (There are 21 images, and we processed 2 similar NG images at our company.) We evaluated a total of 23 images, consisting of 16 training images (2 OK / 14 NG) and 7 untrained images (1 OK / 6 NG), and we are reporting the results.

This is a communication from a trading company we have been dealing with for some time. "Foreign substances" will be detected. We have received inquiries from various customers, including industrial equipment manufacturers, trading companies, and end users. 【Inspection Settings and Results】 When we actually applied image processing, there was some variation in detection depending on the angle, but foreign substances of about 2mm could be detected within a field of view of approximately 250mm. Very small foreign substances, such as tiny black dots or hairs, could be detected within a field of view of about 50mm. 【Software and Equipment Used】 Software Used: DeepSky Learning Field of View: 50mm Minimum Size of Inspection Target: 2mm Number of Inspection Points: 1 location to find foreign substances across the entire screen Camera Resolution: 1.3 million pixels Lens Focal Length: 8mm Lighting: Indoor fluorescent lights

We are considering the introduction of various inspections from a FA control equipment manufacturer. We received an inquiry about automating inspections for "presence of adhesive," "fit claw engagement status," "thermistor adhesive application confirmation," "presence of ring components," "nameplate," "thermistor bending," "presence of screws," "excess/insufficient solder," "presence of lead wires," "solder rise," and "quality of thermal welding." [Inspection Settings and Results] Inspection is likely possible for all inspection items. The initial verification report will be a simple evaluation based solely on pass/fail judgment. The basic concept of image processing using deep learning is to "find what has been taught." For example, when performing "thermistor adhesive application confirmation," you would specify the condition of good products (left) and defective products (right) and conduct learning to distinguish between the presence and absence of adhesive. When considering implementation, please utilize equipment loan services, and ensure that the customer confirms the actual feasibility of inspections.

Social enterprises and social entrepreneurs have been active in various places recently. We received an inquiry from a manufacturer that operates CSR as a business. This time, the evaluation is about counting goldfish in a tank. [Inspection Settings and Results] The image on the left shows the annotation process, which involves enclosing the objects we want to find. The image on the right shows the detection frame in green counting the goldfish. I cropped the video you sent and tested it with software equipped with AI called DeepSky. As a result of the verification, it seems possible to detect the goldfish. However, detection was not possible when they were hidden behind the hose or when two fish overlapped.

In the frozen food manufacturing industry, similar to other industries, there is a process of packaging during shipping that includes labeling the product name and date, as well as securing it with PP bands. This time, we conducted an inspection during the shipping process. We assessed four locations based on three criteria: the printing in two places and whether the PP band was correctly secured. 【Inspection Setup and Results】 Using deep learning image processing, we were able to distinguish between OK and NG cartons as shown in the left image. In the left image, the yellow detection box indicates the date printing, the green detection box indicates the product name, and the light blue detection box indicates the detection of the PP band. The inspection points can overlap in this way. To simulate the actual environment, we placed a bar in front of the subject for evaluation. Since it is difficult to recognize the product name if it is obscured from a direct side view, we angled the camera to ensure that more than half of the carton’s text was visible during the shooting and inspection.

We received a request for a free evaluation from an industrial machinery manufacturer to check if there are any omissions in the stationery set packed in the bag. We will inspect whether there are eight types of stationery inside the bag. Although this time it was stationery, this is an example of an application that can be used in various industries, such as checking if assembly parts are complete or if the instruction manual is included. 【Inspection Settings and Results】 Detection was possible as long as the characteristic parts of the work were not hidden. However, there were cases where detection was not possible if the bag was directly under the reflection, so it is stable to place it under the camera with as few wrinkles in the bag as possible. We were able to distinguish the eight types of work that should be inside the bag effectively. The image shows the inspection screen displaying the judgment result table. In the right image, the missing "instruction manual," labeled "B," is highlighted in red.

The HDD was sent for testing with sample images. 【Inspection Settings and Results】 The left image shows the annotations, while the right image represents the detection frames. By using DeepSky's inspection features, we were able to identify defects in the fabric, such as "discoloration," "thread pulling," and "holes." The overall shooting environment of the images provided was somewhat dark, and as a result, the judgments were generally correct, but there were two misjudgments regarding the floral patterns. If the images could be captured more clearly with better lighting conditions, it would lead to higher accuracy in the assessments. 【Software Used】 Software Used: DeepSky Number of Inspection Points: Entire screen (discoloration, thread pulling, holes)

A manufacturer that handles fittings for metal products and resin products is currently inspecting for holes caused by bubbles underwater. The appearance of bubbles varies in quantity, size, and continuity, and floating debris underwater can resemble bubbles, leading to many misjudgments. This inquiry is regarding a switch from the current image inspection software. We conducted a free evaluation of the four types of images you sent: "large, medium, small, and very small." 【Inspection Settings and Results】 We were able to make good judgments using a method to inspect for the presence of bubbles floating on the water's surface. The left image is an annotated teacher image for training purposes. The right image shows the detection frame display. The rising speed of bubbles underwater is very fast, and with frame-by-frame shooting every 0.3 seconds, there was a risk of missing captures and inspections. DeepSky allows for continuous shooting settings, and inspections without fixed positions during conveyor or work processes are also possible, but depending on the computer's specifications, it typically results in still image frame-by-frame shooting every 0.2 to 0.3 seconds. 【Software Used】 Software Used: DeepSky (Training Version) Number of Inspection Points: One location at the top of the screen (entire water surface)

The curtains installed inside vehicles such as cars, buses, and trucks are folded in an accordion style. If the various components, such as the belts called "hooks" and "tassels," as well as "Velcro," are not attached in the specified positions, the product will not pass inspection. There is a request to automate the current process where workers use jigs to inspect multiple times. 【Inspection Settings and Results】 The inspection was conducted using the inspection software "DeepSky." First, the components to be identified were annotated (left image). To help the software recognize the inspection areas, we annotated 10 good sample images divided into five categories: belts, part A, part B, Velcro, and product tags. Next, the areas where these components should be installed were specified (right image). Since the curtains are accordion-style, a proposal was made to use jigs for fixing in order to capture the workpiece in the correct specified position. It was possible to accurately determine whether the specified components were in the designated locations, marking it as OK if present and NG if not.

It has been said that determining the presence or absence of black components in black workpieces through image inspection is difficult. This time, we conducted an implementation inspection of black accordion-style curtains installed on windows of cars, trucks, and buses. In Hamamatsu City, Shizuoka Prefecture, where our company is located, there are many factories producing automotive parts, and our inspection software is utilized by various automotive parts manufacturers. [Inspection Settings and Results] We conducted the inspection using software that employs AI (Deep Learning). By training the software on the areas we want to detect, it adjusts its own setting parameters and becomes capable of recognition. This inspection software can easily inspect "black defects on black workpieces" and "metal (silver gloss) with metal components or defects (silver gloss)" with simple settings. We encourage you to try it out on our website's "DeepSky Learning Service." We verified it using the black curtains for automobiles that you brought. It was possible to detect the number of installation parts and determine the installation positions of the components. The blue detection frame outside the image indicates a pass for the specified area, while the light green and light blue detection frames inside indicate a pass for the types of components.

The label sealed on the cardboard box containing the product is prone to accidents where it is shipped inverted, and this is one of the cases that has received numerous inquiries. This time, we assume the inspection will take place on a free roller after assembling the cardboard. 【Inspection Settings and Results】 We conducted a free evaluation using the software DeepSky, which utilizes AI (Deep Learning). We used a total of 40 images as training data, capturing the orientation and angle of the sample labels from varying distances with the camera. In the left diagram, the settings are divided into two categories: "OK" and "NG," where if there is even one "NG," it is considered a failure, and up to three "OK" labels are considered a pass. As shown in the right diagram, there were no misjudgments, and the determinations were made with high accuracy. When the judgments were favorable, many proceeded to try the demo unit on a free rental basis. 【Software and Equipment Used】 Software Used: DeepSky Field of View: 400 x 300mm Minimum Size of Inspection Target: 150mm Number of Inspection Points: 1 (label inversion) Camera Resolution: 1.3 million pixels Lens Focal Length: 6mm Distance Between Lens and Product: 900mm Lighting: Indoor fluorescent lights

The use of dedicated trays to place workpieces in each grid during manufacturing and shipping is commonly seen in various situations. While it can be challenging to position workpieces that move within the tray, our inspection software, DeepSk, can assist with such inspections. In the verification with the images you provided, we set up an inspection using AI with DeepSky to distinguish between five types of trays. We trained the model with 70 images (14 images for each of the 5 types) out of a total of 277 images. 【Inspection Settings and Results】 We inspected a total of 277 images, consisting of 70 training images and 207 untrained images. The red box in the right image shows how many of each tray were detected. In this setup, if Tray 1 is detected once, it is set to "pass." The results showed 4 false detections among the untrained images, while the remaining 273 images were successfully detected. By retraining on the falsely detected images, the inspection accuracy can be further improved. The ability to retrain is a strength of inspection software that uses AI. The initial evaluation report on detection capability is kept simple. 【Software Used】 Software used: DeepSky Learning Edition

Out of the 84 sample images you sent (39 NG / 45 OK), 50 images (25 each) were used for training as teacher images. The remaining untrained images were used as test images. 【Inspection Settings and Results】 By using DeepSky's inspection function, we were able to determine the Ito-biki in 0.23 seconds. The results of inspecting the teacher images and the remaining 34 test images showed a correct answer rate of 100%. The number at the top of the image detection frame indicates the AI's confidence level in percentage (number of recognition points). 【Software Used】 Software used: DeepSky Learning Edition

We received 100 sample images from a manufacturer that produces high-quality films for verification. For the free evaluation of DeepSky, it is necessary to send photos of various defective shapes of the work. 【Inspection Settings and Results】 By using DeepSky's inspection function, we were able to detect the presence of various shapes of defects with an inspection cycle of 0.28 seconds per instance. The left image shows the settings, while the right image outlines the areas we want to identify and labels them for learning purposes. - We created a setting where if any NG (defective) item is detected, it is marked as a failure; if only OK items are detected or nothing is detected, it is marked as a pass. By training the software with OK and NG, we have been able to conduct inspections that meet our customers' requirements. Our website offers a trial service for DeepSky. Please take advantage of it. 【Software Used】 Software Used: DeepSky Learning Version

In the pharmaceutical industry, similar to other sectors, products were packed in paper boxes with a specified number and sealed with stickers for shipment. However, incidents of forgetting to apply the seals occurred. We verified this using DeepSky, which can detect the absence of seals regardless of various orientations and angles during the operation. DeepSky is a software that excels in such inspections and is utilized across various industries. 【Inspection Settings and Results】 By using DeepSky's inspection function, we were able to detect the presence or absence of a seal in one location, achieving a judgment time of 0.23 seconds. The inspection time may vary depending on the specifications of the computer. The images were extracted from the report. In the left image, we set it to detect one seal with one type of label as OK, while in the right image, the inspection judgments of "OK" and "NG" were made without false detections. The inspection was conducted with the images provided, but this time we are also considering the environment, such as cameras. The camera and lens will vary depending on the acceptable working distance and field of view.

In convenience stores and dessert shops, it has become common practice to sell items with cutlery included. This time, we will inspect small transparent spoons that are packaged in transparent film on top of bubble wrap. Our company is committed to creating an environment tailored to our customers' inspection situations, providing free evaluations and reports. 【Inspection Settings and Results】 By using DeepSky's inspection features, we easily set up a configuration where if one spoon is present in the screen area, it displays "OK." On the left is the annotation image (the specified image used to teach what to detect), and on the right is the image detecting the spoon. DeepSky can make judgments without fixed positioning, even in situations where it is difficult for a person to distinguish by visual confirmation. 【Software and Equipment Used】 Software Used: DeepSky Field of View: 150 x 120mm Minimum Size of Inspection Target: 70mm Number of Inspection Points: 1 (configured to display "OK" when one spoon is detected and "NG" when no spoon is detected; one label) Camera Resolution: 1.3 million pixels Lens Focal Length: 8mm Distance Between Lens and Product: 430mm Lighting: Indoor fluorescent lights

This is an inquiry from overseas. We have partnered retailers in China and Malaysia. 【Inspection Settings and Results】 By using the "Scratch Inspection" feature of EasyInspector, we were able to determine one location (with a total of two inspection frames) in 0.07 seconds. We applied two types of inspection frames for black and white spots. For detecting even finer fraying, it may be possible to improve detection by increasing the camera resolution or adjusting the lighting. 【Software Used】 Software used: EasyInspector710 Number of inspection locations: 1 (overall)

There was a request for evaluation of image inspection in multiple areas to reduce personnel and improve operational efficiency in the cabinet manufacturing process. 【Inspection Settings and Results】 By using EasyInspector's "Scratch Detection" function and "Presence of Specified Color Inspection" function, we were able to detect 20 differences and determine visually similar items (different products) for each category in less than one second. 【Software and Equipment Used】 Software Used: EasyInspector710 Field of View: Various field of views Minimum Size of Inspection Target: 2mm Number of Inspection Points: 20 Camera Resolution: 5 Megapixels GigE Lens Focal Length: 25mm Distance from Lens to Product: 1200mm Lighting: Linear fluorescent lights, etc. Distance from Lighting to Inspection Item: Not recorded The current 'EasyInspector2' color package can inspect for the presence of specified colors.

Children have loved card games both now and in the past. Many of you may have collected cards when you were young. Some printing manufacturers produce cards specifically for card games. Once packaged, it becomes impossible to know which cards are inside. Therefore, we utilize our EasyInspector to keep a record of the issued cards before packaging. Inspection settings and results By using the "OCR Pro" feature of EasyInspector, we were able to read a single location with a 7-character format (card number) in 0.19 seconds. The OCR Pro feature includes binarization and dictionary learning functions. It can read not only existing fonts but also other types, although it may become difficult to read if the lines of the characters are broken or if adjacent characters are connected. Please contact us for inquiries about the extent of readability.

The instrument manufacturer was experiencing issues with incorrect placement of knobs and dials in their audio products. It is conceivable that human error could occur during visual inspections. They were considering software that could simultaneously inspect multiple factors, such as color differences and incorrect orientations of the knobs. 【Inspection Settings and Results】 Using seven types of image data provided, we were able to detect insertion errors, etc., within our company using the "Master Image Comparison" and "Presence of Specified Color Inspection" functions of "EasyInspector." The inspection time for each part ranged from 0.90 seconds to 0.58 seconds. 【Software Used】 Software Used: EasyInspector310 Number of Inspection Points: 2 to 10 points for each of the 7 images The current 'EasyInspector2' color package can perform inspections using the [Presence of Specified Color] and [Master Image Comparison] features.

At the exhibition, we conducted sample verification of precise measurements of CDs at the request of a trading company that actually tried our inspection software. 【Inspection Settings and Results】 By using the "Dimension and Angle Inspection" feature of EasyInspector, measurements of approximately 62 to 73 microns were achieved. This time, a high-resolution camera with a wide field of view was used. The inspection time was about 0.8 seconds. By narrowing the field of view and reducing the resolution, the inspection time can be shortened. The green thin line in the left image indicates the measured part that was detected. In the right image, we have submitted the evaluation of repeatability accuracy for five repetitions at one location. 【Software and Equipment Used】 Software Used: EasyInspector710 Field of View: Not recorded Minimum Size of Inspection Target: 0.01mm (10 micrometers) Number of Inspection Points: 2 Camera Resolution: 5 million pixels Lens Focal Length: Magnification x0.7-x4.5, Working Distance 52mm, Lens Holder Diameter φ50mm, Total Lens Length 190mm Distance Between Lens and Product: 300mm Lighting: Indoor light The current 'EasyInspector2' MS (MeaSure) package can perform inspections for [Position and Width Measurement] and [Angle Measurement].

This is an inspection to find black spots on the surface of the customer's product, requested by the trading company. They wanted to detect black spots across the entire screen, so we received images and conducted a judgment test. They requested information (coordinates) indicating where the foreign substances are located. 【Inspection Settings and Results】 Detection was achieved using the "Scratch and Defect Inspection" feature of EasyInspector. The screen was divided into 30 sections, creating 30 inspection frames. It is possible to keep records of which inspection frame detected defects, along with images and CSV files. The test was conducted with strict detection settings, so the customer will need to set their own criteria for what constitutes a defect. Our company offers affordable sales by having customers set the parameters themselves. 【Software Used】 Software used: EasyInspector710 The current 'EasyInspector2' color package can be used for inspection with the [Scratch and Defect Detection] feature.

There are tasks involving the application of logo marks from various manufacturers. It is a common mistake to accidentally apply rectangular or square logo marks in reverse. Our company supports you with image inspection to ensure that your important logo is shipped with high design quality and beauty. 【Inspection Settings and Results】 By using the "Comparison with Master Image" feature of EasyInspector, we were able to determine a labeling error in less than 3.56 seconds. The "OK" and "NG" images provided had differences in field of view, brightness, and brightness direction, so we adjusted the "Shift Correction" feature and the settings for "Color Judgment Tolerance Range." It is necessary to prepare the inspection environment during actual operation. (Fixing camera position and orientation, fixing lighting position and intensity, fixing inspection item placement, etc.) 【Software Used】 Software Used: EasyInspector710 The current 'EasyInspector2' color package can perform inspections using the "Comparison with Master Image" feature.

When measuring the number of approximately 60μm thin ceramic sheets handled by an electronics manufacturer, there were disadvantages such as counting errors and the time taken for visual inspection. Therefore, we considered using our image processing software to enable non-contact counting of the sheets. 【Inspection Settings and Results】 Using the "Luminance Change Inspection" function of EasyInspector, we conducted verification by stacking 63 ceramic sheets of 60μm size. If we can clearly show the difference in brightness, inspection is possible. By uniformly illuminating from the front and aligning the product bundle evenly, we were able to count the number of sheets of 60μm size. However, there was a challenge in the stage of setting the inspection frame, as we had to avoid areas with dust and other contaminants. 【Software Used】 Software Used: EasyInspector310 Field of View: 9mm x 12mm Minimum Size of Inspection Target: Approximately 0.06mm (60μm) Number of Inspection Points: 63 Counting of Ceramic Sheets (Thick) https://skylogiq.co.jp/DIY_HowTo/856

It is common to use heat-resistant ceramic sheets in situations where products are handled. They come in various thicknesses, ranging from about 60μm for thin sheets to about 200μm for thicker ones. This time, we solved the problem of measuring the number of 200μm ceramic sheets handled by an electronic component manufacturer, which was previously done manually one by one, by using our software that allows for non-contact measurement of the sheets. 【Inspection Settings and Results】 The verification was done using only photographs. By using EasyInspector's "Luminance Change Inspection," we were able to distinguish between light and dark, and we captured images with 23 thick sheets stacked. Shadows were created between the stacked sheets, making the contrast between light and dark clear, and due to the consistency of the bright and dark lines, we were able to conduct the inspection smoothly. 【Software Used】 Software Used: EasyInspector310 Field of View: 10mm x 13mm Minimum Size of Inspection Target: Approximately 0.2mm (200μm) Number of Inspection Points: 23 Measurement of the Number of Ceramic Sheets (Thin) https://skylogiq.co.jp/DIY_HowTo/860

In the label manufacturing industry, such as for product descriptions, productivity is increased by printing multiple labels at once. On the other hand, issues such as missing prints, smudges, and defects due to black spots have also arisen during printing. This problem of defective shipments is one that many label manufacturing companies face, and image processing software has been used as a means of improvement. 【Inspection Settings and Results】 By using the "Comparison with Master Image" feature of EasyInspector, three inspection frames were set for a single label to establish pass/fail criteria for missing prints, smudges, and black spots, detecting multiple defective areas. In the image above, the smudged area (NG) is highlighted in red, while the OK areas remain unchanged. 【Software and Equipment Used】 Software Used: EasyInspector710 Field of View: 60x 70mm Minimum Size of Inspection Target: Approximately 5mm Number of Inspection Locations: 3 Camera Resolution: 1.3 Megapixels Lens Focal Length: 35mm Distance Between Lens and Product: Approximately 360mm Lighting: Indoor Lighting Current inspections can be performed with the 'EasyInspector2' color package using the "Comparison with Master Image" feature.

Sintering with silver paste causes less damage to the materials compared to soldering, and it can be said that this material will be used more widely in the future as a substitute for solder. 【Inspection Settings and Results】 By using the "Scratch and Defect Inspection" feature of EasyInspector, we were able to detect both chipping and smudging in 0.40 seconds by applying one inspection frame to the entire area. The size (resolution) per pixel is approximately 7μm (0.007mm). In actual operation, our "sm＠rtROBO" is used to move the inspection area while being inspected by a single camera. 【Software and Equipment Used】 Software Used: EasyInspector Field of View: 14 mm Minimum Size of Inspection Target: Approximately 7μm (0.007mm) Number of Inspection Points: 1 Camera Resolution: 5 million pixels Lens Focal Length: Not recorded Distance Between Lens and Product: 95mm Lighting: Backlight illumination The current 'EasyInspector2' color package can be used for [Scratch and Defect Detection].

At the request of an LED lighting manufacturer, we conducted a verification test for dirt detection. This test aims to detect dirt such as film-like dust that is difficult to see with the naked eye. 【Inspection Settings and Results】 We received four types of samples and were able to detect three types of dirt. However, for the sample with a bumpy surface finish, we were unable to detect dirt under the same imaging conditions. How to capture the defective areas becomes a crucial point in the inspection. The inspection frame was set as a single frame for the entire area, with an inspection cycle of 2.03 seconds. 【Software and Equipment Used】 Software used: EasyInspector710 Field of view: 64 x 51 mm Minimum size of inspection target: 0.2 mm Number of inspection points: 1 Camera resolution: 10 million pixels Lens focal length: 25 mm for high pixels Distance between lens and product: 260 mm Lighting: LED lighting (illuminated from both sides)

This is a judgment test based on the submitted images. If the inspection target can be captured clearly, it can generally be said that inspection is possible. 【Inspection Settings and Results】 By using the "Dimension Angle Inspection" feature of EasyInspector, we were able to measure the angle at one location and make a judgment in 0.09 seconds. 【Software Used】 Software Used: EasyInspector310

The packaging design of food products often changes in small parts or colors during campaigns, and there have been instances of mistakenly shipping packaging due to similar designs, which has caused some trouble. This time, we determined the differences in design and the differences in the same design materials. 【Inspection Settings and Results】 By using the "Comparison with Master Image" feature of EasyInspector, we detected design differences in the entire imaging screen frame and were able to identify visually similar products (different items) in under 50 seconds. 【Software and Equipment Used】 Software Used: EasyInspector710 Field of View: A4 Size Minimum Size of Inspection Target: 5mm Number of Inspection Points: 1 Camera Resolution: Our Company Scanner Current 'EasyInspector2' color package can be inspected using the "Comparison with Master Image" feature.

This was an inquiry from a trading company interested in our software EasyInspector, asking whether standard features could accommodate their needs, and if not, whether customization would be possible. 【Inspection Settings and Results】 By using the "Dimension Angle Inspection" feature of EasyInspector, we measured two dimensions and made a judgment in less than one second. Inspection frame 001 measured the inner diameter of a circle (251.50 pixels), and inspection frame 002 measured the black width (59.50 pixels). In this verification, measurements were possible with standard features, and while customization can be accommodated according to customer requests, it will incur additional costs. 【Software Used】 Software Used: EasyInspector200 Number of Inspection Points: 2

During the inspection stage of wood cutting, you were considering cutting a board measuring 2m in length and a certain width from 5mm to 160mm while avoiding defective parts and cutting at the right timing. 【Inspection Settings and Results】 For knots, we used the "Color Comparison Inspection / Presence or Absence of Specified Color" function of EasyInspector. The specified color was set to the color of the knots. An inspection frame the size of one kamaboko board was set from the edge of the board, and this area was divided into four parts for the presence or absence color inspection. To capture the roughness of the board, we dimmed the indoor lighting and illuminated it from an angle, which allowed us to detect the roughness. The inspection item was "Scratch Inspection." No alignment correction was applied for the inspection of the flowing boards.