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Visual inspection software Product List and Ranking from 21 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

91～105 item / All 470 items

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[AI Image Inspection Case] Solder Inspection on Circuit Board

[AI Image Inspection Case] Solder Inspection on Circuit Board

We will conduct an inspection to ensure that the chips and solder on the substrate are correctly placed!

This is a request for jumper checks through soldering work on the substrate. A sample implementation board measuring 80mm x 40mm has been provided, along with specified camera and lens. The flow meter manufacturer has contacted us through our website to discuss standardization of the work. 【Inspection Settings and Results】 Five inspection frames were set up for each inspection location. Of the five inspection frames, three were configured using EasyInspector's "Comparison with Master Image" inspection function to detect whether chips are mounted, among other things. Two of the five inspection frames were set up using the "Presence/Absence of Specified Color" function to detect only when there are no chips or solder. If defective parts exceeding the reference value are detected, the result will be deemed "Fail."

  • Image Processing Software

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[AI Image Inspection Case] Detection of Center Misalignment and Burrs in Metal Products

[AI Image Inspection Case] Detection of Center Misalignment and Burrs in Metal Products

The AI image inspection software determines burrs and center misalignment in metal products' holes!

There are two requests: to detect and identify burrs in a hole (with a hole diameter of 2mm) and to determine the misalignment of a part with text centered on a 50mm square plate. This inquiry came from a manufacturer of air conditioning equipment considering an automatic inspection system. 【Inspection Settings and Results】 By using the EasyInspector "Presence of Specified Color Inspection" function, we were able to detect the differences in the two holes and determine the misalignment in 1.49 seconds. We introduced a 30-day trial service available for download from our website, allowing them to try it out and provided guidance on the setup method. We suggested changing the "Correction Position" (red frame and light blue frame), the values for "Color Judgment Tolerance Range," and the "Pass/Fail Criteria Value" to "Both." Additionally, we noted that there are areas where misjudgment may occur due to uneven brightness (the edges appear darker). We advised them to reconsider the position and brightness of the lighting.

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Red Eye Defects in Soldering

[AI Image Inspection Case] Inspection of Red Eye Defects in Soldering

We will improve the variability in the judgment criteria for "good products" and "defective products" in visual inspections through automated inspections!

There have been numerous inquiries regarding substrate inspections, and a considerable number of customers are already utilizing our services. This inquiry comes from a harness manufacturer that is active in cutting-edge wiring technology. In visual inspections conducted by workers, there is often variability in the criteria for determining "good" and "defective" products among inspectors. The more ambiguous the judgment criteria are, the more inconsistent the inspection results tend to be. We propose stable and efficient production through image inspection. 【Inspection Settings and Results】 By using EasyInspector's "comparison with master images" feature, we were able to detect differences in the positions of defects and identify similar-looking items (different products) with red eyes. The inspection time was approximately 3 seconds. We believe that inspections are relatively easy if the lighting environment is stable. The rule-based traditional image inspection software used this time and the AI (deep learning) image inspection software each have their own strengths and weaknesses. Please contact us to determine which inspection method is more suitable.

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Hole Position and Presence in Rubber Flanges

[AI Image Inspection Case] Inspection of Hole Position and Presence in Rubber Flanges

The AI image inspection software detects and determines the presence and position of holes in circular rubber flanges!

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.

  • Image Processing Software

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[AI Image Inspection Case] Quantification of Surface Processing

[AI Image Inspection Case] Quantification of Surface Processing

We quantify the surface roughness of the coating layer on resin products using AI image inspection software!

The cellulose fiber resin manufacturer has delicate surface processing technology. This time, we received an inquiry about quantifying the surface roughness of the coating layer through inspection, and we decided to test whether the surface roughness could be determined through image inspection instead of visual confirmation (sensory evaluation). 【Inspection Setup and Results】 By using the "Presence or Absence of Specified Color" function of EasyInspector, we were able to quantify the differences in texture at one location (overall). The results were better when judged using EasyInspector, which allows for detailed settings including stability and color tolerance ranges. The settings for how much to detect as black and what shade to consider as black were done using EasyInspector. 【Software Used】 Software Used: EasyInspector710 The current 'EasyInspector2' color package can be used for inspection with the "Presence or Absence of Specified Color."

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Typos and Misspellings in Kraft Paper Bags

[AI Image Inspection Case] Inspection of Typos and Misspellings in Kraft Paper Bags

The AI image inspection software evaluates the printed content, such as text, on kraft paper bags!

A manufacturer that produces craft paper bags for brown rice was considering using our image inspection software to evaluate the printed content, such as text, on the craft paper bags at their factory. Currently, multiple factory personnel are inspecting the printed content each time, and there are instances where mistakes, such as typos, go unnoticed, leading to incorrect products being delivered to customers. Therefore, they were thinking of transitioning to machine-based inspection to prevent the outflow of incorrect items and reduce the burden on personnel. [Inspection Settings and Results] By using the "Comparison with Master Image" feature of EasyInspector, we were able to inspect the differences in four areas of text in 0.23 seconds. We set the field of view to capture the entire printed area for verification. In the left image, the parts that differ from the master image (settings screen) are highlighted in red as NG (not good) detections. [Software Used] Software used: EasyInspector The current 'EasyInspector2' color package allows for inspection using the "Comparison with Master Image" feature.

  • Image Processing Software

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[AI Image Inspection Case] Determination of Checklist Signatures

[AI Image Inspection Case] Determination of Checklist Signatures

We will conduct an inspection using AI image inspection software to check for the presence or absence of signatures on the production management checklist!

The manufacturer, which designs various devices such as medical equipment and infrastructure, has been a business partner for some time. This time, we received a request for a simple verification inspection to check the signatures on the production management checklist. 【Inspection Setup and Results】 By using the "Presence of Specified Color Inspection" feature of EasyInspector, we detected the presence or absence of signatures in four locations. The left image shows the master image (inspection frame setup screen). It was set up so that if the color of the detected signature within the installed frame is within the reference value, it is marked as OK; otherwise, it is marked as NG. The right image shows that the blue frame indicates pass, while the red frame indicates fail. The areas with signatures are judged as OK, while the empty areas are judged as NG. By setting up an inspection frame in the areas to be checked, the presence or absence of entries can be inspected. A maximum of 999 inspection frames can be installed. 【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] Size of Annotation

[AI Image Inspection Case] Size of Annotation

I will share the key points for "annotation" to teach the AI where to detect.

This is a support case for a customer who has actually implemented DeepSky. We provided guidance to increase the detection rate. The "annotation" used to teach the AI where to detect is crucial for inspection accuracy. The image below shows annotations that had a low detection rate. We advised that annotations that are too large or too small decrease the detection rate. 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. 【Inspection Settings and Results】 We set the judgment to "fail" after detecting one or more defective areas. (The inspected images were unknown images different from the trained teacher images.) We inspected 32 unknown images (16 good products / 16 defective products) and achieved 30 correct judgments and 2 incorrect judgments (we failed to detect defects, and all good products were correctly judged). *We also inspected the teacher images, but there were 2 false detections. It is likely that increasing the number of training images and the training time will improve detection. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Washer Presence Inspection

[AI Image Inspection Case] Washer Presence Inspection

The AI image inspection software detects the presence or absence of washers!

We received a request for a simple verification from a major general electronics manufacturer that has contacted us before regarding DeepSky. The presence or absence of washers is an area where DeepSky excels. We recommend trying out the web version to experience AI (deep learning) more closely. 【Inspection Settings and Results】 From the images provided, we used 10 OK images and 10 NG images, totaling 20 images, as training data. We then evaluated 5 OK images and 5 NG images, totaling 10 images, from the remaining images as unknown data. In this verification, there were no false detections, and we were able to correctly classify all 30 images. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Washer's Curling Presence or Absence

[AI Image Inspection Case] Inspection of Washer's Curling Presence or Absence

We will process the texture of the washer to detect the sagging parts!

This is a specific consultation regarding the operation after a simple verification from a manufacturer of precision processed products such as automotive parts. We will create a setup to detect the blurred areas of a washer through image processing. In the free sample evaluation, we will first conduct a simple verification using the samples or images provided and report the verification results. During the simple verification, we will evaluate whether the desired detection/judgment is possible using our in-house equipment. If detection or judgment is successful in the simple verification, we recommend conducting a test (feasibility verification) assuming actual operation, where we will evaluate processing time and judgment accuracy. If feasibility verification is to be conducted, you can choose to continue with our company (for a fee) or verify using our loaned equipment at your company. [Inspection Setup and Results] Upon verification with the samples provided, it was possible to distinguish the presence or absence of blur. Taking images slightly diagonally rather than directly from the side showed better differences in appearance due to the presence of blur. We used 13 images as training data, framed the areas we wanted to inspect, and labeled all relevant areas of each image as "Blur Present" or "No Blur." We executed approximately 500 steps of learning and set it up so that it would be marked as a failure when a "No Blur" label was found.

  • Image Processing Software

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[AI Image Inspection Case] Detection of Screw Marks

[AI Image Inspection Case] Detection of Screw Marks

The AI image inspection software detects scratches and dents on screws!

Manufacturers dealing with various types of motors have shown interest in our AI-based image inspection software "DeepSky" since its launch. This time, it will assess scratches and dents on screws. Our inspection software has over 2,000 examples of image inspections. We have numerous inspection cases published, including metals, plastics, food, electronic circuit boards, and pharmaceuticals, so please check our website to see if there are similar cases to the inspections you are considering. 【Inspection Settings and Results】 As a result of verification using the samples we received, we were able to detect scratches, allowing us to report favorable inspection results. We can respond free of charge to requests for demonstrations, web meetings, and demo unit loan services. The loan period for demo units is approximately one week, but availability is limited, so there may be delays. The image on the left shows annotations, while the image on the right shows the detection frame with a favorable assessment.

  • Image Processing Software

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[AI Image Inspection Case] Detection of Terminal Defects

[AI Image Inspection Case] Detection of Terminal Defects

The AI image inspection software detects the condition of terminal crimping and determines it as OK/NG!

We received an inquiry from a manufacturer of automotive and construction machinery harnesses. They would like to conduct inspections using our software based on a request from the client, as they cannot confirm the state of terminal crimping through image inspection. A free preliminary evaluation was conducted. In the free sample evaluation, we first perform a simple verification using the samples or images provided, and we report the verification results. The simple verification assesses whether the desired detection/judgment can be achieved using our internal equipment. If detection or judgment is possible in the simple verification, we recommend conducting a test (feasibility verification) assuming actual operation, and evaluating processing time and judgment accuracy. If feasibility verification is to be conducted, you can choose to continue with our company (for a fee) or use our loaned equipment for verification at your company. 【Inspection Settings and Results】 We conducted verification using our "DeepSky" equipped with deep learning capabilities. As a result, we were able to detect and judge each NG item. The left image shows detection frames indicating two types of abnormalities. The right image displays the number of detections that can be confirmed on the settings screen. However, in this verification, there were some instances of false judgments. Therefore, I believe it will be necessary to increase the number of verifications in the future.

  • Image Processing Software

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[AI Image Inspection Case] Presence or Absence of Cable Ties

[AI Image Inspection Case] Presence or Absence of Cable Ties

We will conduct an inspection of the presence or absence of cable ties using AI image inspection software!

In wire harness manufacturing, there is a process of bundling multiple harnesses together. While traditional "EasyInspector" is often used for inspecting electronic components, combining it with "DeepSky," which utilizes Deep Learning, can sometimes lead to higher quality production. 【Inspection Settings and Results】 Even with harnesses of various colors, we were able to accurately assess the bands regardless of their front or back. In the images provided this time, the large field of view made detection more challenging. We advised that taking close-up shots would improve accuracy. The numbers in the images represent the AI confidence percentage (number of recognition points). 【Software Used】 Software Used: DeepSky The current EasyInspector2 is equipped with AI capabilities and can handle inspections of defects with various colors and shapes. We will suggest software tailored to your inspection targets.

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[AI Image Inspection Case] Judgment of Circuit Board Assembly

[AI Image Inspection Case] Judgment of Circuit Board Assembly

It detects good IC leads and poor solder conditions (lifting)!

We received a contact from a manufacturer that produces hardware and software for printed circuit board manufacturing and assembly. They are considering whether it is possible to perform image diagnostics to assess the quality of soldering after components are mounted on the printed circuit board and soldered. 【Inspection Settings and Results】 Based on the images provided, we were able to identify the NG (not good) areas as NG. In this verification, we used a total of 9 images: 6 images (NG1-3, OK1-3) as training data to create the model, but since we could not detect NG7-8, we added these 3 images to the training set. As a result, we were able to detect correctly, but since the occurrence of NG was fixed at the far right, further verification is needed to determine if detection is possible with other patterns.

  • Image Processing Software

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[AI Image Inspection Case] Judgment of Injection Molded Products

[AI Image Inspection Case] Judgment of Injection Molded Products

Detects "scratches," "welds," and "dot debris" on metal formed products.

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

  • Image Processing Software

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