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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

196～210 item / All 471 items

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[AI Image Inspection Case] Inspection of Foreign Objects and Dirt on Non-Woven Fabrics

[AI Image Inspection Case] Inspection of Foreign Objects and Dirt on Non-Woven Fabrics

The AI image inspection software detects foreign substances and dirt in non-woven fabric!

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.

  • Image Processing Software

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[AI Image Inspection Case] Verification Inspection of Thermistor Adhesive Application

[AI Image Inspection Case] Verification Inspection of Thermistor Adhesive Application

We will conduct various tests using AI image inspection software!

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.

  • Image Processing Software

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[AI Image Inspection Case] Detection of Black Spots on Oyster Flesh and Eggs

[AI Image Inspection Case] Detection of Black Spots on Oyster Flesh and Eggs

We will conduct defect assessment of shucked oysters using AI image inspection software!

Seafood and fruits have a rough similarity in shape, but in image inspection, their shapes are not stable, and variations in size and ripeness can change their color and bulge, making them difficult to assess. Our inspection software, DeepSky, excels in inspecting items with slightly different shapes. This time, we evaluated the defect detection of shucked oysters based on an inquiry from a manufacturer that produces automation robots used in food factories. Since it is not the shipping season for oysters, the inspection was conducted using the images provided. 【Inspection Settings and Results】 Upon verifying the sample images you sent, we were able to detect eggs and black spots. The inspection was performed using software that employs AI (Deep Learning). By training the software on the areas we wanted to detect, it adjusts its own setting parameters and learns to recognize them. Our software does not only detect based on color; it also incorporates texture (surface quality) into its judgment. Additionally, there is no need for fixed positioning, allowing for detection even if multiple oysters flow on a conveyor in various positions and orientations.

  • Image Processing Software

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[AI Image Inspection Case] Judging Defects in Fabric

[AI Image Inspection Case] Judging Defects in Fabric

We conducted a free evaluation of the detection of "discoloration," "thread pulling," and "holes" in three types of fabric: "quilt fabric," "flower pattern," and "solid color."

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)

  • Image Processing Software

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[AI Image Inspection Case] Inspecting holes in a workpiece using underwater bubbles

[AI Image Inspection Case] Inspecting holes in a workpiece using underwater bubbles

We will check if there are any holes in the flexible hose!

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)

  • Image Processing Software

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[AI Image Inspection Case] Identifying Types of Trays

[AI Image Inspection Case] Identifying Types of Trays

We will conduct inspections to distinguish between five types of trays using AI image inspection software.

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

  • Image Processing Software

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[AI Image Inspection Case] Adhesive Stringing

[AI Image Inspection Case] Adhesive Stringing

We have received a request from a camera manufacturer to detect "adhesive stringing" that occurs during manufacturing.

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

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  • Image Processing Software

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[AI Image Inspection Case] Film Identification

[AI Image Inspection Case] Film Identification

We distinguish various defects that occur during film manufacturing. This time, we created and set labels for seven defects and good products.

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

  • Image Processing Software

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[AI Image Inspection Case] Color Identification Inspection

[AI Image Inspection Case] Color Identification Inspection

We identify the colors of internal parts of automotive products and make judgments! We detect similar different items because they look almost the same!

The seat designs of automobiles, buses, airplanes, and other vehicles may vary in design and size due to different specifications, and the internal components also change depending on the grade. In the case of similar products, accidents occurred where similar but different items were shipped because they looked almost identical. 【Inspection Settings and Results】 An evaluation was conducted using DeepSky. On the left is the annotation image (the teacher image that teaches the part to be detected), and on the right is the inspection result image. This time, since it was a work that could not be externally leaked, our company used a model and proposed an annotation method. Currently, for consideration of implementation, we are offering a free rental service for demo units. The number of demo units is limited, so depending on the reservation status, there may be delays. We kindly ask you to make your reservations as early as possible. 【Software and Equipment Used】 Software used: DeepSky Field of view: 100 x 80mm Minimum size of inspection target: 5mm Number of inspection points: 4 (Label 8) Camera resolution: 1.3 million pixels Lens focal length: 12mm Distance between lens and product: 450mm Lighting: Indoor fluorescent lights

  • Image Processing Software

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[AI Image Inspection Case] Multiple Inspections Including the Back Tack of the Cabinet

[AI Image Inspection Case] Multiple Inspections Including the Back Tack of the Cabinet

This is an inspection regarding the back tacker, front door color, peel, internal label, presence or absence of screws, Urea screws, and the implementation of the connection part and bump part.

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.

  • Image Processing Software

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[AI Image Inspection Case] Card Recognition OCR

[AI Image Inspection Case] Card Recognition OCR

We are utilizing our EasyInspector before packaging to record the issued cards.

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.

  • Image Processing Software

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[AI Image Inspection Case] Counting the Number of Glass Sheets

[AI Image Inspection Case] Counting the Number of Glass Sheets

The AI image inspection software counts the number of stacked glass sheets.

Some production technology personnel believe that it is difficult to inspect work with high visibility, such as glass, due to halation. Our company also proposes inspection methods using cameras, lenses, and lighting that can be used for photography. 【Inspection Settings and Results】 By using the "Luminance Change Inspection" function of EasyInspector, we were able to verify the number of stacked glass sheets and make a judgment in 0.04 seconds. We detected the black areas and accurately counted the number of sheets. However, we could not make accurate judgments (detect differences in brightness) in areas where light was not clearly shining, making lighting an important factor in this case as well. 【Software Used】 Software Used: EasyInspector300 Number of Inspection Points: 1

  • Image Processing Software

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[AI Image Inspection Case] Inspecting Logo Mark Inversion

[AI Image Inspection Case] Inspecting Logo Mark Inversion

Detects mistakes in the placement of rectangular and square logo marks!

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.

  • Image Processing Software

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[AI Image Inspection Case] Panel Hole Presence Inspection

[AI Image Inspection Case] Panel Hole Presence Inspection

We will conduct an inspection of a panel with two large holes and 27 small holes using AI image inspection software!

In the automotive manufacturing industry, the panels to be installed have many holes, and because there are similar panels, if there is any issue with the presence or position of even one hole, the product cannot be deemed acceptable. Therefore, we considered using image recognition technology. [Inspection Settings and Results] We included a panel with two large holes and 27 small holes within the field of view. For the larger holes, we set up the EasyInspector's "Color Comparison Inspection" feature to detect the "pink color" of the holes. For the smaller holes, we used the "Defect Inspection" feature to detect white defects. The holes themselves were clearly captured using backlight illumination, allowing for accurate inspection even if the position of the holes was off by 50mm. The red areas on the right side of the image indicate where the holes were detected.

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Bumps on Coated Surfaces

[AI Image Inspection Case] Inspection of Bumps on Coated Surfaces

The AI image inspection software detects and determines defects in automotive parts caused by painting errors!

When inspecting automotive parts after painting, there are instances where defects caused by painting errors result in the shipment of defective products. We wanted to enable the detection of these defects through image inspection. In automotive parts manufacturing, post-painting defect inspection is considered crucial for product assembly, leading us to consider the introduction of image processing to ensure accuracy. 【Inspection Setup and Results】 Using the "Scratch and Defect Inspection" feature of EasyInspector, we set up inspection frames to detect bright pixels compared to surrounding pixels and dark pixels at the same position, allowing detection regardless of which type is present. Even in cases where the background has a striped pattern, we were able to detect defects as small as 1mm by modifying the settings.

  • Image Processing Software

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