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

Last Updated： Aggregation Period:Nov 12, 2025～Dec 09, 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 12, 2025～Dec 09, 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 12, 2025～Dec 09, 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. Appearance inspection software "MELSOFT VIXIO" 藤川伝導機
  4. 4 Appearance inspection software "MELSOFT VIXIO" 藤川伝導機
  5. 5 [AI Image Inspection Case] Reading Text on Nameplates スカイロジック

Visual inspection software Product List

181～195 item / All 471 items

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[AI Image Inspection Case] Water Leak Inspection from Seal Area

[AI Image Inspection Case] Water Leak Inspection from Seal Area

Detect and assess water leaks from the seal area using AI image inspection software!

The company responsible for the energy-saving and new energy support department of the group company is experiencing issues with water leaks from the mechanical seal section of their pumps, and they reached out for consultation through our website. Our website offers trial versions of the image inspection software "EasyInspector," "EasyMonitoring," and "DeepSky." We encourage you to try them out to help resolve your issues. [Inspection Settings and Results] We imported the images you provided into our image inspection software and conducted an inspection. If the water droplet appears white against a black background, it should be easier to detect. However, if the surrounding environment is bright and the water droplet becomes transparent, it may become difficult to detect the transparent areas. We used the "Presence of Specified Color Inspection" feature in EasyInspector to detect and evaluate the glowing parts of the water droplets. It seems that operation with "EasyMonitoring" is also possible; this software can send notifications (with images) to your smartphone via email.

  • Image Processing Software

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[AI Image Inspection Case] Confirmation of Check Sheet (2)

[AI Image Inspection Case] Confirmation of Check Sheet (2)

We will check the check sheets filled out by people using AI image inspection software!

The fact that 'there are inevitably oversights by operators during visual inspections' can occur not only in factory production processes but also in other areas. This inquiry comes from an industrial machinery manufacturer, but the verification of check sheets filled out by humans is a common issue across all industries. [Inspection Settings and Results] We printed the data from the check sheet you sent and conducted tests. We verified the presence or absence of checks on paper. By using EasyInspector's 'OCR Pro' feature, we were able to read five holes or symbols in just 0.62 seconds. After a simple verification, we will conduct an in-house demonstration. Additionally, we offer a web video demonstration on our website, a 60-day trial software download, and a free evaluation service for inspection items, all aimed at providing information and support to help customers easily implement the inspection system. We look forward to your inquiries and hope you take advantage of the above services. If you have any requests or questions, please feel free to contact us anytime.

  • Image Processing Software

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[AI Image Inspection Case] Detection of Battery Orientation

[AI Image Inspection Case] Detection of Battery Orientation

After storing 12 or 24 batteries in the gold box, it is a check to ensure there are no mistakes in the orientation of the batteries.

We received an inquiry from a battery manufacturer stating, "There is a white + mark on the cover, and we want to determine if there is any mistake in orientation based on its position." They also expressed a desire to take photos while holding a tablet and camera by hand. 【Inspection Settings and Results】 By using the "Presence of Specified Color Inspection" feature of EasyInspector, we were able to determine the orientation of the parts. We reported on a simple verification. Inspection frames were set up in multiple locations (this time at six locations: including two NG locations). We set "white" as the specified color and conducted the inspection based on the extent to which the specified color was detected within the inspection frame. When using EasyInspector, it is essential to fix the camera. While it is possible to take photos with a tablet, it needs to be secured. 【Software Used】 Software used: EasyInspector710 The current 'EasyInspector2' color package can perform inspections for the "Presence of Specified Color."

  • Image Processing Software

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[AI Image Inspection Case] Judgment of Paper Tubes

[AI Image Inspection Case] Judgment of Paper Tubes

We will perform judgment of paper tubes using AI image inspection software.

This is a free simple verification based on the sample images you provided. It is from a manufacturer of industrial control equipment with whom we have had transactions in the past. In image inspection, the imaging environment is a very important point. This time, the accuracy of the inspection varied depending on the captured images. 【Inspection Settings and Results】 We were unable to obtain good results with the images as they were, so we cropped only the areas to be evaluated and conducted the verification. As a result, in the case of the images we verified this time, if we could set the field of view appropriately for the judgment of the paper tube, it was possible to make a judgment. Since there were few OK images, we created mirrored images to use for verification. We used a total of 20 images for training, consisting of 10 OK images and 10 NG images. For the validation of the judgment, we used 2 OK images and 2 NG images that were not used in the training, for a total of 4 images. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Overflow of Food Ingredients

[AI Image Inspection Case] Overflow of Food Ingredients

We will inspect and determine the extent to which the food in the tray is overflowing.

We conducted a simple inspection of the extent of food overflow in trays from a food manufacturer using the rule-based "EasyInspector" and reported our findings. However, detecting overflow on trays with a blue background and white patterns proved difficult, so we decided to re-verify using "DeepSky," which utilizes AI (deep learning). Our company website offers a free web trial of the inspection software. You can actually experience the inspection software that uses AI (deep learning). 【Inspection Settings and Results】 We created several patterns of defective conditions using the samples we have on hand and were able to detect ingredient overflow by training on those images. However, in this case, various patterns of ingredients and defects are expected, so a large amount of training data is necessary to achieve sufficient detection results. With the version upgrade in 2021, we were able to incorporate a data augmentation feature for training images. We will continue to add various convenient features and develop user-friendly inspection software from a practical perspective. 【Software Used】 Software Used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Strawberry Harvesting Season

[AI Image Inspection Case] Strawberry Harvesting Season

We will determine the harvest time of strawberries using AI image inspection software!

At the request of a manufacturer of industrial equipment, we conducted a simple evaluation to determine the harvest timing of strawberries. We used 58 sample images for the inspection. (34 strawberries were classified as OK and 24 as NG) 【Inspection Settings and Results】 As a result, all "harvestable strawberries" were correctly detected. Teacher Images: Correct Judgment 100% (20/20) Incorrect Judgment 0% (0/20) Unlearned Images: 94% (32/38) 6% (2/38) Total: 96% (56/58) 4% (2/58) However, among the strawberries classified as NG, two strawberries that appeared close to OK when viewed by the human eye were mistakenly classified as harvestable. (1) Still pinkish in color, therefore not harvestable... NG1 (Pink) (2) Still white or green, therefore not harvestable... NG2 (White or Green) (3) Strawberries that can be harvested... Harvest OK By creating three types of labels and training the software, it will adjust its own setting parameters and improve recognition. The images are annotated.

  • Image Processing Software

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[AI Image Inspection Case] Waste Liquid Treatment Judgment

[AI Image Inspection Case] Waste Liquid Treatment Judgment

We will conduct waste liquid processing (monitoring of coagulation state) using AI image inspection software!

We received an inquiry from a manufacturer of various machine parts regarding bearings. They currently monitor the treatment of waste liquid through visual inspection, and it is said that when aggregation occurs from the state shown in the left four images below to the state in the right four images, it is considered acceptable. Although there is a roof, it is not an indoor environment, and the imaging conditions for inspection change depending on the time and weather. 【Inspection Settings and Results】 We set 24 images, including the ones above, as training images, annotated them all, and were able to make judgments. With the version upgrade in 2021, we were able to incorporate a data augmentation feature for training images. We will continue to develop inspection software that is user-friendly and practical by adding various convenient features. The strength of AI (deep learning) image inspection software compared to traditional rule-based image inspection software is its ability to perform inspections even with differences in brightness and imaging environments. Inspection software can be beneficial in various industries and processes. 【Inspection Software】 Software used: DeepSky

  • Image Processing Software

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Inspection Technique: Defective Positions of Teacher Images

Inspection Technique: Defective Positions of Teacher Images

We will provide solid support regarding settings such as what kind of teacher images to use!

We received an inquiry from a customer who manufactures automotive parts and industrial machinery regarding the settings of DeepSky. They asked, "I thought the system was searching the entire screen based on the characteristics of the annotated area I set, but do you also remember any tendencies about where it tends to appear in the field of view?" 【Inspection Settings and Results】 We arrange squares of the same color and size and annotate only the upper part for training (see the image in the top left). In the image on the top right, we can find only the upper part. In the lower left image, we can also find only the upper part. However, in the right image, we could not find anything from the lower part. If the system can only find data that closely matches the teacher due to overfitting, the situation changes a bit. However, the position of the items we want to detect, such as defects, greatly affects inspection accuracy. It is necessary to use teacher images with various positions, orientations, and angles. As of 2021, DeepSky has been equipped with a teacher image augmentation feature. We can augment images of defects that rarely appear by flipping or rotating them in all directions and using brightness or Gaussian noise.

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  • Image Processing Software

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[AI Image Inspection Case] Position of Press Felt Parts

[AI Image Inspection Case] Position of Press Felt Parts

We will check if the installation position of the clip for the press felt parts is correct!

Press felt parts for automotive components may vary in the presence or absence of holes and the positioning of parts 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. This time, we will verify whether the installation position of the clips is correct. 【Inspection Settings and Results】 We were able to distinguish between two types of clips in the overall view. It seemed challenging to determine the presence of the fastener's tacker or the text on the label from the overall view, so I suggested that using two cameras for one workpiece would be a more practical approach. The image shows the setup for inspecting whether the correct type of clip is in the correct position. The outer frame indicates the correctness of the installation position, while the inner frame determines whether the part is correct. 【Software Used】 Software Used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Adhesive Application Cut-off

[AI Image Inspection Case] Adhesive Application Cut-off

We will inspect for adhesive application defects using AI image inspection software!

This is a request for evaluation from a manufacturer of audio systems such as speakers. Before the inquiry, line workers were conducting visual inspections of about 10,000 units a day. Due to the black resin and dark adhesive, the work had characteristics that made it very difficult to see, both visually and with cameras. 【Inspection Setup and Results】 Since it was difficult to conduct inspections with EasyInspector, we used a product that utilizes deep learning called DeepSky. Initially, we tried training with the existing images, and detection was possible; however, the number of data points was small, and the images were very difficult to inspect, so I believe more detailed verification is needed in terms of accuracy. Nevertheless, detection was achieved to some extent, and I am reporting the above image as documentation of that process. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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Technical Support: Accuracy and Resolution

Technical Support: Accuracy and Resolution

We also provide support for annotation methods and capturing teacher images that improve detection accuracy.

We received an inquiry regarding the detection method of DeepSky from a pharmaceutical company we have been in contact with for some time. 【Test Settings and Results】 Image B is one of the four divided parts. The resolution is simply one-fourth. Assuming the area of defects in the entire field of view is 1, the area of defects in the entire field of view of Image B would be 4. Since the "target object" is significantly larger in the overall image, it becomes easier to detect in Image B, even though its resolution is lower. This is why the importance of resolution itself is lower in deep learning. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Detection of Foreign Objects in Bento Boxes

[AI Image Inspection Case] Detection of Foreign Objects in Bento Boxes

We attempted a verification assuming the contamination of "hair," "plastic pieces," "vinyl pieces," and "insects."

In the food industry, contamination with foreign substances has long been a significant issue. This time, we conducted inspections using a software called DeepSky, which utilizes AI (Deep Learning). The image in the upper left is called an annotation, where we train the software to recognize specific areas (foreign substances) by adjusting its own setting parameters. The software detects "hair," "plastic," and "insects" in the images. However, it was unable to identify the insect mixed in with the sesame seeds on the rice. It is necessary to capture a clear distinction between the sesame and the insect. Software used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Detection of Foreign Objects in Cylindrical Filters

[AI Image Inspection Case] Detection of Foreign Objects in Cylindrical Filters

Detects foreign substances (fibers, dust, hair, and particulate matter) in cylindrical filters.

In visual inspections conducted by workers, there is often variability in the criteria for determining "good products" and "defective products" among inspectors, and the more ambiguous the criteria, the more inconsistent the inspection results tend to be. Please consider a stable and efficient inspection using an image inspection system. The image on the left shows the environment with the camera, lens, and lighting. The image on the right depicts the task known as "annotation," which involves framing the areas to be detected. By "training" the framed areas, we were able to detect the target objects within the inspection images. The inspection items were placed on a rotating platform, and detection verification was conducted while rotating. During the verification, it was assumed that the inspection would take about 60 seconds per item. As for dust, it could not be visually confirmed as defective, so settings and detection could not be performed. If defective parts cannot be photographed, the inspection becomes challenging. Creative solutions are needed for capturing images.

  • Image Processing Software

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[AI Image Inspection Case] Presence or Absence of Transparent Varnish

[AI Image Inspection Case] Presence or Absence of Transparent Varnish

We will inspect the presence or absence of transparent varnish using AI image inspection software!

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

  • Image Processing Software

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[Technical Support] How to Generate Parameters for DeepSky

[Technical Support] How to Generate Parameters for DeepSky

If you have any questions about results or settings that you cannot accept, please feel free to contact us.

The grain dryer manufacturer experienced the web trial of our inspection software DeepSky, which uses AI (deep learning) from our website, but did not obtain satisfactory inspection results. We provided guidance on the method for generating parameters. While personnel familiar with deep learning inspections can conduct inspections smoothly, it is natural that first-time users may not achieve the desired results. If you have any questions about unsatisfactory results or the settings, please feel free to contact us. 【Inspection Settings and Results】 We enclosed the target areas to be detected in frames and labeled them by type. This time, we used seven pieces of training data consisting of five types: the numbers 1 to 4 and one without a number. All images correctly identified the targets. 【Software Used】 Software used: DeepSky Number of inspection points: 2 locations, recognizing the numbers in two areas on the screen.

  • Image Processing Software

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