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Visual inspection software Product List and Ranking from 22 Manufacturers, Suppliers and Companies

Last Updated： Aggregation Period:Nov 19, 2025～Dec 16, 2025
This ranking is based on the number of page views on our site.

Visual inspection software Manufacturer, Suppliers and Company Rankings

Last Updated： Aggregation Period:Nov 19, 2025～Dec 16, 2025
This ranking is based on the number of page views on our site.

  1. スカイロジック Shizuoka//software
  2. エーディーディー Kyoto//software
  3. 藤川伝導機 Tokyo//Industrial Machinery
  4. 4 三機 Creative Lab Aichi//robot
  5. 5 株式会社Roxy Aichi//IT/Telecommunications
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Visual inspection software Product ranking

Last Updated： Aggregation Period:Nov 19, 2025～Dec 16, 2025
This ranking is based on the number of page views on our site.

  1. AI General Purpose Appearance Inspection Software 'EasyInspector2' スカイロジック
  2. AI visual inspection software "DeepSky" スカイロジック
  3. [AI Image Inspection Case] Reading Text on Nameplates スカイロジック
  4. 4 Appearance inspection software "MELSOFT VIXIO" 藤川伝導機
  5. 5 [AI Image Inspection Case] Tears in Shrink-Wrapped Products スカイロジック

Visual inspection software Product List

256～270 item / All 471 items

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[AI Image Inspection Case] Monitoring of Underground Tank Meters

[AI Image Inspection Case] Monitoring of Underground Tank Meters

Automate the monitoring of underground tank meters! Image inspection will help improve productivity.

The industrial machinery manufacturer has requested to automate the monitoring of underground tank meters. Monitoring the meters, even on-site, requires manual checks in rain or snow, which is time-consuming and labor-intensive. Image inspection can help improve productivity in such cases. Since the reading of the underground tank meter could not be done with the usual meter reading function, a method based on the number of lit lamps was used, allowing for threshold determination as shown in the attached document. (For example, if 4 lamps are lit, it can be read as 40%, and if 6 lamps are lit, as 60%.) Thresholds can be set for each frame, allowing for configurations such as defining a certain percentage as an abnormal value. Using the "Meter Reading" function of EasyInspector, five inspection frames could be evaluated in 0.52 seconds. [Software Used] EasyInspector (formerly EasyInspector) The current 'EasyInspector2' CP (Control Panel) package [Meter Display Reading] can be inspected with 'EasyMonitoring2'.

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[AI Image Inspection Case] Dimension Inspection of Urethane

[AI Image Inspection Case] Dimension Inspection of Urethane

In the free evaluation service, we provide lighting, cameras, lenses, and other equipment to test whether the defects you want to see can be detected.

This is a request for a simple verification from a manufacturer of custom automatic machines that specializes in high precision and high-speed transport. By using the "Dimension Angle Inspection" feature of EasyInspector, it was possible to measure the dimensions of four sides using eight frames. In this setup, the dimensions are determined by the difference in values detected by the two frames. The left image shows the detection of the two frames. The right table indicates that Frame 004 is marked as "Fail" due to being outside the tolerance. Since we used a 5-megapixel camera this time, the width is 2592 pixels. With a field of view of approximately 700mm, the size of 1 pixel is calculated as "700mm ÷ 2592 pixels = 0.27". The sample product you sent was identifiable with the above precision, but ideally, we would like to conduct inspections with a tolerance of about 0.1 for 1 pixel. For example, with a 20-megapixel camera, the width would be 5400 pixels, making the size of 1 pixel 0.12, allowing for more accurate measurements. Therefore, verification with another sample is necessary. The current takt time was 0.44 seconds.

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[AI Image Inspection Case] Inspection of Urethane and Clips

[AI Image Inspection Case] Inspection of Urethane and Clips

We conducted a clip protrusion inspection and a dimension inspection of the urethane!

We received a message from an industrial equipment manufacturer stating, "We were considering whether we could conduct inspections with your image inspection software 'EasyInspector' and learned that there is a free evaluation service available." The items they want to inspect are two types: checking for excess urethane and detecting leftover ends of clip inspection tape, as well as checking for tape overflow (with a margin of error of 1mm). It was possible to conduct inspections that could be confirmed visually. The verification results for the clip overflow inspection and the urethane dimension inspection were both validated using EasyInspector's dimension angle inspection. For the other inspection item, the clip tape ends and urethane holes, it seems necessary to narrow the field of view (with multiple inspections) using a different software called DeepSky. [Software Used] EasyInspector710 (formerly EasyInspector) The current 'EasyInspector2' MS (MeaSure) package can conduct inspections with angle measurement.

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[AI Image Inspection Case] Inspection of Sponge Deviation and Presence of Rubber

[AI Image Inspection Case] Inspection of Sponge Deviation and Presence of Rubber

Detects sponge bias and the presence or absence of rubber!

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.

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[AI Image Inspection Case] Pitch Inspection of Rubber Products

[AI Image Inspection Case] Pitch Inspection of Rubber Products

Rubber molded parts detect differences in the mounting position of clips!

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.

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[AI Image Inspection Case] Rubber Product Gloss Inspection

[AI Image Inspection Case] Rubber Product Gloss Inspection

Detect the gloss of rubber products in 8 locations and determine visually similar counterfeit products (defective)!

We received an inquiry from a manufacturer specializing in FA machinery, who is considering implementing our system in the inspection department of their factory in Malaysia. Our company has agents in Malaysia and China, and we plan to expand our sales channels overseas in the future. By using the "Scratch and Defect Inspection" feature of EasyInspector, we were able to detect glossiness in eight rubber products and determine visually similar defective items in less than 0.26 seconds. The "Scratch and Defect Inspection" feature was used to detect any white or black dots or lines within something that should be uniform. Additionally, we selected the "ring" inspection frame to match the shape of the products. 【Software and Equipment Used】 Software Used: EasyInspector710 (formerly EasyInspector) Field of View: 3 x 2mm Minimum Size of Inspection Target: 3mm Number of Inspection Points: 8 Camera Resolution: 1.3 Megapixels Lens Focal Length: 35mm Distance Between Lens and Product: Approximately 50mm Lighting: Indoor Fluorescent Light The current 'EasyInspector2' color package can be used for [Scratch and Defect Detection] inspections.

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[AI Image Inspection Case] Barcode Reading and Labeling

[AI Image Inspection Case] Barcode Reading and Labeling

Detects whether the barcode reading and label display are correct!

We will conduct a simple verification to check if the barcode reading and label display are correct. Using the "Comparison with Master Image" feature of EasyInspector, we conducted the verification with the samples provided, but there were many false detections when the labels were affixed to the trays, making it difficult to perform the inspection properly. Since we only received good products, the images of defective products were created by our company. Barcode reading was possible even when the labels were affixed to the trays. 【Software Used】 EasyInspector (formerly EasyInspector) The current 'EasyInspector2' color package can be used for inspection with the "Comparison with Master Image" feature.

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[AI Image Inspection Case] Verification of Food Overflow

[AI Image Inspection Case] Verification of Food Overflow

Detects food that is overflowing from the tray!

If the side dishes lined up in the supermarket overflow from their trays, there will be concerns about leakage and poor presentation, which may lead to unsold items. This time, we received a request for a simple verification regarding the overflow of food placed in trays from a food manufacturer. By using EasyInspector's "Presence of Designated Color" feature, we were able to inspect one area (the entire screen) in 0.39 seconds. The left image shows the designated color settings, where we set the color of the food that may overflow. The right image shows the mask settings (specifying non-detection pixels), where the specified area will no longer detect the designated color. 【Software and Equipment Used】 Software Used: EasyInspector (formerly EasyInspector) Field of View: Approximately 10 x 8 mm Minimum Size of Inspection Target: 5 mm Number of Inspection Points: 1 Camera Resolution: 1.3 Megapixels Lens Focal Length: 6 mm Distance from Lens to Product: Approximately 165 mm Lighting: Ring Lighting Distance from Lighting to Inspection Item: Approximately 120 mm The current 'EasyInspector2' color package can be used for inspection with the "Presence of Designated Color" feature.

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[AI Image Inspection Case] Reading Analog Meters

[AI Image Inspection Case] Reading Analog Meters

I will read the analog gauges in the factory!

There was an inquiry about wanting to remotely check the analog instruments in the factory. ■ Indoor Meters For indoor analog meters, I believe reading them is possible. However, if inspections are to be conducted at night, it is necessary to maintain consistent brightness between day and night. ■ Outdoor Meters Outdoor meters will likely require lighting. Additionally, even with lighting, there may still be variations in brightness and shadows between day and night. It seems necessary to install something to enclose the meter or a roof-like structure for the meter. Another reason why reading outdoors is difficult is due to power supply issues. By using EasyInspector's "Meter Reading" function, it was possible to read measurement values from one location. Regarding the difference in light intensity, the image on the right can be read, but the left image is misreading a different part as the needle, resulting in an incorrect judgment. Creating a stable imaging environment for reading is crucial.

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[AI Image Inspection Case] Counting Plastic Parts

[AI Image Inspection Case] Counting Plastic Parts

Count plastic parts with AI!

Counting parts is a common inquiry, and this time it involves a plastic parts manufacturer. We feel that the demand for inspection automation due to labor shortages is increasing year by year. We conducted inspections using a software called DeepSky, which utilizes AI (Deep Learning). By training the software on the areas we want to detect, it adjusts its own settings and learns to recognize them. Upon verifying the samples we received, we found that counting was possible. However, for samples without specified trays, when they were in close contact or overlapping, we were able to validate the inspection items by making adjustments. Regarding inspection cycle time, the EasyInspector we submitted previously has a shorter cycle. However, since counting items not placed in trays is difficult to determine, we decided to validate this time using DeepSky. The inspection cycle time with DeepSky depends on PC specifications, but it is approximately 0.3 seconds per image (1 inspection). With DeepSky, you can utilize higher-level software that supports counting on conveyors. It is designed to accommodate various types of counting.

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[AI Image Inspection Case] Text Inspection of Silk Screen Printing

[AI Image Inspection Case] Text Inspection of Silk Screen Printing

We will conduct inspections for issues such as missing text in silk screen printing through image inspection!

You sent us photo samples of defective conditions that occasionally occur in the silk printing process. Is it possible to introduce inspections for issues like missing characters in silk printing through image inspection? This is a consultation regarding that. We will conduct a simple inspection for "missing" and "fading." By using the "comparison with master image" function of EasyInspector, we were able to conduct the inspection. We performed verification on the sample with the smallest visible defect among the samples we received. As a result, under limited conditions (*), we were able to detect the defect of missing characters. However, outside of those conditions, due to the nature and shape of the product, we mistakenly detected many areas that were not defective. *The limited conditions refer to when the inspection was conducted by focusing on one specific area of the printed part, so if we try to operate under the same conditions, the inspection range will likely be quite limited.

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Inspection Techniques: Features of DeepSky

Inspection Techniques: Features of DeepSky

Introducing the features of the AI image inspection software "Deepsky"!

DeepSky is an image inspection software that uses so-called AI (Deep Learning). By training it on the parts you want to detect, the software adjusts its own setting parameters and learns to recognize them. Here are three features that specifically differentiate it from traditional methods. *We will use the inspection of washer scratches as an example.* ▼ No need for positioning of the inspection target (Figure 1: No scratches, OK) (Figure 2: Scratches, NG) The fact that positioning is unnecessary means that inspections can be performed with the same settings even if the number of washers on the screen varies. This is effective for inspection targets that are difficult to secure. ▼ Can detect even with changes in brightness (Figure 3) It can detect scratches even when the lighting is this dim. This is effective in cases where it is difficult to suppress reflections from metal parts or when dealing with products that come in multiple colors.

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[Inspection Technique] DeepSky Function 1 – Area Judgment

[Inspection Technique] DeepSky Function 1 – Area Judgment

Introducing one feature of the AI image inspection software "Deepsky" from a practical perspective!

Function: OK/NG Judgment by Area This is a function that judges not only "how many items were detected in the image" but also "where they were detected." Depending on the application, it may not be possible to make a correct judgment based solely on the total count on the screen. For example, in the case where there are two capacitors positioned alternately, as shown below. In this case, if the orientations of the capacitors are reversed, they should be deemed unacceptable; however, as for the total count, there would be one with the polarity mark on top and one with it on the bottom, resulting in both being considered acceptable. (Figure 1) In such cases, judgments are made by dividing into areas. As shown in the dotted box below, predetermined areas are set up, and the count judgment is performed for each area. (Figure 2) Within each area: "If one upward-facing capacitor is detected, it is OK." "If even one downward-facing capacitor is detected, it is NG." Settings like these are made. (Figure 3) This allows for correct NG judgment even for defective items that have the same count but are positioned alternately. (Figure 4)

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[Inspection Technique] DeepSky Feature 2 – Auto Annotation

[Inspection Technique] DeepSky Feature 2 – Auto Annotation

Introducing the practical features of the AI image inspection software 'DeepSky' from a field perspective!

▼Feature: Auto Annotation This is a function that automatically executes annotations. It was previously a beta feature, but it has become an official feature starting from Ver. 2.2.0.0. Many of you may know that annotations are very important in object recognition. However, when there are many target objects in an image, the amount of work required for annotation increases, and as the workload increases, mistakes such as forgetting to annotate or making errors inevitably rise. If annotations are forgotten, DeepSky adjusts parameters to determine that "this is something that should not be found (even though it looks similar) for the same object," which can significantly lower the recognition rate of the object, leading to various issues. (See Figure 1: Annotation Forgetting) This is where the auto annotation feature comes into play. By using auto annotation, with just a click of a button, it automatically suggests annotations like "Based on the previous training data, you would want to annotate here, right?" (See Figures 2, 3, and 4)

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【Technical Support】DeepSky can assist you even in food production sites.

【Technical Support】DeepSky can assist you even in food production sites.

AI (deep learning) detects hair, foreign objects, and blemishes on fruits and vegetables mixed into bento boxes!

We have received several inquiries from various food production and processing sites. We are now able to handle cases that were difficult for EasyInspector (formerly EasyInspector), such as detecting hair and foreign objects mixed in bento boxes, which further demonstrates the wide range of capabilities of our AI (deep learning) functions. DeepSky allows us to teach it with a broader scope regarding what we want to detect, making it possible to identify foreign objects that do not conform to a specific shape. If there are items that are currently being checked by human eyes and you wonder, "Can this be inspected?" please feel free to contact us.

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