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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

346～360 item / All 470 items

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[AI Image Inspection Case] Defects in Pressed Assembly Products

[AI Image Inspection Case] Defects in Pressed Assembly Products

Detect defects in press assembly products from valve parts!

Even manufacturers that globally deploy valve parts are considering our inspection software. Our software is utilized in specialized and intricate products across various industries. 【Inspection Settings and Results】 The task of enclosing the parts to be detected in a rectangle for parameter generation is called annotation. Each part is registered with the Label name shown in the right image, and it is set up so that if there is even one defect, it will be deemed a failure. We would like you to consider stable production through the automation of inspections, especially for products that require a high level of solution. 【Software and Equipment Used】 Software Used: DeepSky Learning Edition Field of View: Approximately 78 x 62 mm Minimum Size of Inspection Target: 2 mm Number of Inspection Points: 1 point, detecting defects from the entire screen Camera Resolution: 1.3 million pixels Lens Focal Length: 12 mm Distance Between Lens and Product: Approximately 150 mm Lighting: Ring lighting Distance Between Lighting and Inspection Item: Approximately 100 mm above

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Chipped and Skimmed Circular Saws

[AI Image Inspection Case] Inspection of Chipped and Skimmed Circular Saws

The AI image inspection software detects and distinguishes chips and gaps in the saw blades!

This is an external inspection at a metal parts manufacturer that produces chip saws. Although the inquiry was about dimensional angle inspection, upon discussion, it became clear that the request was for the detection of chipping and scratches. 【Inspection Setup and Results】 For the time being, we verified larger chipping and scratches, and it seemed that they could be detected without any issues. If it is something that can be detected by DeepSky (our AI inspection software), we can automatically conduct continuous inspections as shown in the video, stopping and notifying when a defect is found. We also conducted a demonstration where the saw's rotation was manually operated, and an NG was issued when an abnormality entered the field of view. 【Software Used】 Software Used: DeepSky Number of Inspection Points: 1 location, entire screen

  • Image Processing Software

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[AI Image Inspection Case] Judging Scratches on Glossy Metal Plates

[AI Image Inspection Case] Judging Scratches on Glossy Metal Plates

Detecting scratches on metal plates with AI image inspection software!

We will test the detection of scratches on metal sheets based on an inquiry from a copper manufacturer. This involves verification using provided photos. A copper sheet approximately 1mm thick is rolled up like toilet paper, and we are considering whether to inspect it while capturing images at low speed during the rolling process or to stop the line to take pictures and then inspect. In the first stage of a simple free evaluation, we were able to detect the scratched areas, but we are mistakenly detecting white areas on the image that are not scratches, making it difficult to distinguish. If we can improve the lighting to illuminate as wide an area as possible uniformly, inspection seems feasible. The images provided were used as "training data," and this is the result of the processing. Since these are training data images, we can make highly accurate judgments. The numbers represent the AI's confidence level percentage, referred to as "recognition points." [Software Used] Software: DeepSky Learning Version Number of inspection points: 1 across the entire screen

  • Image Processing Software

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[AI Image Inspection Case] Determination of the Front and Back of a Nut

[AI Image Inspection Case] Determination of the Front and Back of a Nut

We will distinguish between the processed surface (front) and the unprocessed surface (back) of the nut!

Even with the same material and similar shapes, there are many cases where texture can be perceived and judged. We also sell to trading companies and industrial equipment manufacturers. Please feel free to contact us. 【Inspection Settings and Results】 It was possible to determine the presence or absence of processing (front or back) of the nuts through image processing using deep learning. Since there is almost no difference in the images between those that had chips removed and those that did not, they were treated as the same for inspection purposes, and a total of 20 pieces, 10 processed and 10 unprocessed, were used as training data, resulting in good judgment. Deep learning is one of the machine learning methods that teaches computers to learn the thinking processes that humans naturally perform. Our inspection software, DeepSky, is designed with this AI technology. 【Software Used】 Software Used: DeepSky Learning Version Number of Inspection Points: 1 (Determining whether the workpiece is front or back)

  • Image Processing Software

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[AI Image Inspection Case] Determining Gear Chipping

[AI Image Inspection Case] Determining Gear Chipping

The AI image inspection software will determine the chipping of gears!

We received an inspection request from a manufacturer of production equipment. The inspection is for determining the chipping of a metal product, specifically a "gear." They sent us images of 20 defective items and 5 acceptable items. For some workpieces that are difficult to send as samples, verification can sometimes be done through photographs. 【Inspection Settings and Results】 Using DeepSky's inspection function, we were able to accurately determine the chipping of the metal workpiece (gear). The process of setting up the areas to be detected by enclosing them in rectangles is called annotation; in this case, we only enclosed the defective parts for training. The images show the detection frames. The numbers indicate the AI's confidence level percentage (number of recognition points). If the number of recognition points is low or if there are misjudgments, further training can be conducted. DeepSky, released in 2020, is easy to set up, does not require fixed positioning, can detect various types of defects in shapes, and is particularly good at detecting defects in shiny workpieces like metal products. We have reported numerous evaluations of metal product inspections. 【Software Used】 Software Used: DeepSky Learning Version Number of Inspection Points: 1 (detecting defects from the entire screen)

  • Image Processing Software

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[AI Image Inspection Case] Judgment of Metal Cutting Chips

[AI Image Inspection Case] Judgment of Metal Cutting Chips

We will use AI image inspection software to determine defects caused by chip adhesion on metal workpieces!

We received a request for evaluation from a manufacturer of production equipment. This pertains to the judgment of defects caused by metal chips adhering to the workpiece. The DeepSky system released in 2020 is known for its ease of setup, the elimination of the need for fixed positioning, its ability to detect various shapes of defects, and its proficiency in detecting defects in shiny metal products, leading to numerous evaluations of metal product inspections. 【Inspection Setup and Results】 To determine the "metal chips inside the workpiece's hole," we made adjustments to the lighting. We attempted an inspection that captures images inside the hole, and this time, the bottom of the hole was clearly visible, allowing for accurate judgment. The initial evaluation is a report on whether simple detection is possible; however, depending on the position and shape of the defects, there may be instances where detection is challenging. After our free evaluation, we would like customers to directly experience the accuracy and setup methods before implementation, so we kindly ask you to utilize our free demo unit lending service. 【Software Used】 Software Used: DeepSky Learning Edition Minimum Size of Inspection Target: 2mm Number of Inspection Points: 1 (finding defects from the entire screen) Lighting: Coaxial Illumination

  • Image Processing Software

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[AI Image Inspection Case] Determining the Wear of a Saw Blade

[AI Image Inspection Case] Determining the Wear of a Saw Blade

We will determine the wear of the saw blade using AI image inspection software!

It seems that manufacturers are struggling with various defect detections for saw blades, just like with other metal products, including issues such as unprocessed areas, dents, compression marks, chips, and foreign objects. For image inspection of shiny metal workpieces, please contact our company. This time, we are detecting wear on saw blades based on a verification request from a trading company we have been dealing with for some time. 【Inspection Settings and Results】 The judgment was made based on the NG images provided. Although we were unable to make sufficient settings due to difficulties in comparing with good products, we were still able to make some judgments, though some were misjudged. We report this as one result. The images show the defective areas that were successfully detected, highlighted with a light blue frame. The numbers indicate the confidence percentage (number of recognition points) from the inspection software. 【Software Used】 Software used: DeepSky Learning Edition Number of inspection points: 1 location (finding the worn area from the entire screen)

  • Image Processing Software

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[AI Image Inspection Case] Detection of Wrinkles, Dents, and Impressions on Metal Balls

[AI Image Inspection Case] Detection of Wrinkles, Dents, and Impressions on Metal Balls

The AI image inspection software detects defects in metal products such as "ball wrinkles," "tapered area dents," and "tapered area impacts"!

We will verify whether we can detect "dents on the ball," "dents on the tapered section," and "indentations on the tapered section" based on a request from an industrial equipment manufacturer. By creating a striped pattern on the reflective part of the ball, it becomes easier to identify the dented areas due to the resulting step difference. 【Inspection Settings and Results】 We inspected 25 images, including 10 images of "dents on the ball," 5 images of "dents on the tapered section," 5 images of "indentations on the tapered section," and 5 images of "good products." Out of the 25 images, 22 were correctly identified as either good products or defective parts. The three images of "dents on the tapered section" could not be recognized as defective parts; however, considering that in actual operation, multiple shots (around 3 times) are expected to be taken during one full rotation, it does not necessarily mean that the defective parts that were not recognized in the verification will always go unrecognized.

  • Image Processing Software

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[AI Image Inspection Case] Grommet Presence Inspection

[AI Image Inspection Case] Grommet Presence Inspection

We will inspect the presence or absence of eyelets and rubber parts using AI image inspection software.

In the manufacturing of metal press products such as automotive parts, there is often a process for attaching eyelets. This time, we will conduct inspections for the presence or absence of eyelets and rubber parts. We received good products and defective products that do not have all the parts attached. 【Inspection Settings and Results】 Using DeepSky's inspection function, we were able to classify good products and defective products without all the parts as negative by using them as teachers and marking random positions where eyelets are missing. It is set to pass only when the correct quantity of eyelets (and rubber parts) is detected. If even one eyelet is missing, it will be marked as negative due to quantity mismatch. If you want to check "which eyelet is missing," it is possible to use the area specification function, but for now, we conducted the inspection with settings to determine only OK or NG. The left image shows the work of enclosing the parts we want to teach, called "annotation." The right image is the detection frame image, where the numbers represent the AI's confidence level percentage, referred to as "recognition points."

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Welding Defects

[AI Image Inspection Case] Inspection of Welding Defects

Detects defects such as bead misalignment, blowholes, and tears in the joints of metal welded parts!

Metal welded parts, such as automotive components, often have defects like bead misalignment, blowholes, and tears due to specification differences, which have been common inquiries for a long time. Previously, inspections were conducted using EasyInspector with fixed positioning, but with the inspection capabilities of DeepSky released in 2020, it has become easy to set up inspections without fixed positioning. 【Inspection Settings and Results】 The target areas to be detected were enclosed in frames and labeled by type. A total of 14 images were used as training data, consisting of 8 OK images and 6 NG images. The learning process was executed for 2,000 steps, and the graph converged in about 16 minutes. The time may vary depending on the specifications of the PC used. In this inspection, since we are detecting the defective areas that were trained, it was set so that if even one defect is detected, it would be considered NG (only OK when the count is from 0 to 0), allowing for the detection of welding defects.

  • Image Processing Software

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[AI Image Inspection Case] Detection of Scratches on Screws

[AI Image Inspection Case] Detection of Scratches on Screws

The AI image inspection software detects scratches on screws!

Even manufacturers who specialize in precision cutting and grinding processes are utilizing our inspection software for high-quality precision machining technology and quality management systems. 【Inspection Settings and Results】 As a result of verification using the samples provided, it was possible to detect scratches on the screws. The inspection was conducted using a software called DeepSky, which employs AI (Deep Learning). By training the software to recognize the desired scratches, it adjusts its own setting parameters to identify them. Five samples were photographed from different angles and the opposite side, resulting in 22 training images, with the left image set as the reference. The right image shows the detection frame. 【Software and Equipment Used】 Software Used: DeepSky Field of View: 30 x 25mm Minimum Size of Inspection Target: 0.2mm Number of Inspection Points: 1 overall Camera Resolution: 1.3 million pixels Lens Focal Length: 35mm + 5mm close-up ring Distance Between Lens and Product: Approximately 160mm Lighting: Indoor fluorescent lights

  • Image Processing Software

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[AI Image Inspection Case] Inspection of scratches and dents on pressed parts

[AI Image Inspection Case] Inspection of scratches and dents on pressed parts

We inspect defects such as scratches and dents on pressed products using AI image inspection software!

Deformation of the insertion port in cassette gas products can lead to significant accidents. We hope that our inspection software will be useful for your safety. In this free evaluation, we used the software DeepSky, which employs AI (Deep Learning) for inspection. By training the software on the areas we want to detect, it adjusts its own setting parameters and learns to recognize them. [Inspection Settings and Results] By using DeepSky's inspection capabilities, we determined multiple types of defects. We assessed the presence or absence of each type of defect based on scratches, shadows, and reflections, achieving a judgment time of 0.33 seconds. The labels were divided into three categories: scratches, shadows, and reflections, and we set up the software to learn the specific defects we wanted to identify. The method of specifying these defects and how to capture the images is crucial for image inspection.

  • Image Processing Software

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[AI Image Inspection Case] Character Recognition of Semiconductor Laser Chips

[AI Image Inspection Case] Character Recognition of Semiconductor Laser Chips

We will perform character recognition of semiconductor laser chips using AI image inspection software!

The ferrule made from polyphenylene sulfide (PPS), a thermoplastic resin, is a connector that allows for the high-precision, high-density mass connection of multi-core optical fibers. However, a resolution of 0.01μm in image processing is essential, making it a very challenging situation, and there was a request for hole pitch measurement of the MT ferrule. Additionally, there were multiple inquiries regarding character recognition of semiconductor laser chips and measurement of the amount of adhesive droplets. 【Inspection Settings and Results】 For character recognition of semiconductor laser chips, by using the "OCR Pro" function of EasyInspector, it was possible to read numbers at one location (15 characters) and make a judgment in 0.2 seconds. 【Software Used】 Software Used: EasyInspector Number of Inspection Locations: 1 (15 characters)

  • Image Processing Software

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[AI Image Inspection Case] Blu-ray Recorder Rear Display Inspection

[AI Image Inspection Case] Blu-ray Recorder Rear Display Inspection

The rear display of the Blu-ray recorder with the same shape detects faded printing defects!

It is common in any industry to manufacture seemingly similar products on a single line. Even in audio equipment manufacturing, there have been incidents where the rear display of Blu-ray recorders of the same shape was mistakenly swapped, or defects occurred where the printing was smudged and unreadable. 【Inspection Settings and Results】 By using EasyInspector's "Comparison with Master Image" function, we were able to detect six printing discrepancies and determine visually similar counterfeit products (different items) in 0.36 seconds. Since a printing deviation of approximately ±1mm is considered acceptable, it would be deemed unacceptable under the current inspection settings, necessitating some adjustments. For example, one could set the verification level to a larger value or set the deviation correction for each inspection frame to "automatic" to perform corrections at various points, while conducting separate dimensional angle inspections for positional deviations. 【Software and Equipment Used】 Software Used: EasyInspector310 Field of View: Approximately 200 x 130mm Minimum Size of Inspection Target: 2mm Number of Inspection Points: 6 Camera Resolution: 3 million pixels Lens Focal Length: 12mm Distance from Lens to Product: 280mm Lighting: Indoor fluorescent lights

  • Image Processing Software

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[AI Image Inspection Case] Laser Diode Serial Number Character Recognition

[AI Image Inspection Case] Laser Diode Serial Number Character Recognition

We will conduct serial number character recognition inspection of laser diodes using AI image inspection software!

We received an inquiry regarding the optical character recognition of serial numbers on laser diodes from an electronic component manufacturer. Our inspection software, EasyInspector, enhances processing speed by converting characters into two colors, black and white, to clearly define the boundary between the inspection target and the background (binarization). By using the "OCR Pro" feature of EasyInspector, we were able to detect the presence and positional differences of three holes, allowing us to determine visually similar counterfeit products (different items) in 0.05 seconds. This evaluation was based on the images provided. The image on the right shows the display when inspection results are recorded in a CSV file. In this way, all inspection results can be recorded, and it is also possible to keep a visual record.

  • Image Processing Software

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