Visual inspection software Product List and Ranking from 21 Manufacturers, Suppliers and Companies

[AI Image Inspection Case] LED Lighting Determination

The AI image inspection software determines the pass or fail of 48 LED locations.

This is an inquiry from a metal processing manufacturer that has already implemented our solutions. They are engaged in various business developments and will conduct a simple verification of LED lighting inspection this time. In the free sample evaluation, we will first perform 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 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, where we will evaluate processing time and judgment accuracy. If feasibility verification is to be conducted, you can choose to continue with us (for a fee) or use our loaned equipment for verification at your company. 【Inspection Settings and Results】 By using the "Presence of Specified Color Inspection" function of EasyInspector, it was possible to determine the pass/fail of 48 LED locations. This time, capturing the light edges clearly was challenging, so we either reduced the exposure or narrowed the aperture to capture the particles as distinctly as possible. After that, we can only search for values that can be successfully detected while tightening the color judgment tolerance. We will summarize and report the setting values for reference, but please note that the environments differ between us and you, so having the same values does not guarantee the same results.

Company：スカイロジック
Price：Other

Image Processing Software

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[AI Image Inspection Case] Dimension Assessment of Automotive Parts

We will inspect whether the dimensions of automotive parts are within tolerance.

We received sample images from an automotive parts manufacturer. We will report whether the dimensions are within tolerance through a free simple verification. In the inspection process, automation is being promoted to pursue stable inspection accuracy and cost reduction. 【Inspection Settings and Results】 By using EasyInspector's "Dimension Angle Inspection" function, we were able to confirm the tolerance of a dimension at one location (left settings screen). The inspection cycle time is 0.62 seconds. We are measuring the edges of the hole and flat surface in inspection frames 001 and 002 (right detection screen). The software can calculate the difference in the X coordinate values from 002 to 001, and it is possible to convert the dimensions to mm. In the free sample evaluation, we will first conduct a simple verification using the received samples or images and report the verification results. In the simple verification, we will evaluate whether the desired detection/judgment is possible with our in-house 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 conducting feasibility verification, you can choose whether to continue with our company (for a fee) or to use our rental equipment for verification at your company.

Company：スカイロジック
Price：Other

Image Processing Software

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[AI Image Inspection Case] Forgotten Paint on Rubber Hose

We will inspect and determine the presence or absence of identification paint on the end face of the black rubber hose!

We received a contact from a manufacturer considering a poka-yoke device for forgetting to paint rubber hoses. When there is identification paint (white, yellow, red, green) on the end face of a black rubber hose approximately Φ20, it will indicate OK; if there is no identification paint, it will indicate NG during the inspection of body parts. Our website offers a free trial of inspection software online. You can actually experience the inspection software that uses AI (deep learning). 【Inspection Settings and Results】 It is possible to determine that there is one blue mark on the inspection screen. In the case of red, the count for "red" will be one, and if there is one each of white and green, they will be counted separately. Since we have trained the system to recognize the absence of marks as "none," the above image is judged to have one "none." At this stage, we are simply counting how many of each there are, so if there is more than one "none," we will set the system to determine it as a failure. 【Software Used】 Software used: DeepSky

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Detection of Capsules

Detect capsules with AI image inspection software!

We received a direct request from a customer who manufactures medical capsules and were conducting a preliminary verification. However, since our company does not sell FA machines, we decided to proceed with the verification through the manufacturer of the control equipment that the customer is dealing with. Our company is located in Hamamatsu City, Shizuoka Prefecture, and we have many industrial machine manufacturers as clients, primarily in the automotive industry. We have developed easy-to-integrate inspection software that is sold outright, and we have received numerous repeat orders from manufacturers of industrial machines and systems. 【Inspection Settings and Results】 As a result of processing the images received, the first image was detected normally. The second image had one false detection and one non-detection. After retraining, it was able to detect normally. After verification, we will conduct a demo at our company to explain the inspection environment and operation methods, and we will lend out demo units so that you can actually check the setup methods and inspection accuracy. 【Software Used】 Software used: DeepSky

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Detection of Tire Types

The AI image inspection software determines the type of tire!

We have received a simple free evaluation to determine the types of tires, such as automotive parts. This time, we tried sending photos, but sending actual samples would provide the most reliable results. In that case, you will need to send several good products and the defective work you want to detect. Please contact us for details. 【Inspection Settings and Results】 The left image shows the annotation process (the task of enclosing the parts you want to teach). The right image displays the OK screen. The tire text is being evaluated by DeepSky. We also have the capability to record images and CSV files, making integration with surrounding systems easy. The number of detectable types that can be registered per product type ranges from 1 to 1000 (here, "types" refers to classification names used during annotation, such as "screw," "desiccant," "tomato," etc. If the number of detectable types exceeds 100, the detection rate may decrease.) 【Software Used】 Software used: DeepSky

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Specific Marks on Glass

We will determine whether the mark printed on the glass installed in the automobile is correct!

A manufacturer of industrial equipment, with whom we have had a relationship for some time, has been actively inquiring about "DeepSky" since its release. Our company is accepting verification and support requests from our 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】 Out of 42 images, 21 were used as training images. We inspected a total of 42 images, consisting of 21 training images and 21 untrained images. The result was that we were unable to detect only one untrained image, while all others were successfully detected. Images that were blurry or had faint prints were generally able to be judged. If we can stabilize the captured images further, we believe the accuracy could improve. 【Software Used】 Software used: DeepSky

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Detection of Defects in Desiccants

Detecting defects in desiccants with AI image inspection software!

This is a case that we continued to consider after a simple verification. In image inspection, the shooting environment is very important. If we can accurately capture defects, inspection is possible with a high probability. It is necessary to prepare the inspection environment within the operational range of the camera, lens, and lighting. [Inspection Settings and Results] While continuing our discussions, we found that using a backlight allowed us to clearly capture the entrapment (defect), which is expected to improve the detection rate, so we were able to provide a good report. The device used this time is a tracing table that costs several thousand yen, so I believe that sufficient effects can be obtained even with lighting that is not specifically for inspection, as long as it is a surface light source. Our company website offers a free web trial of the inspection software. You can actually experience the inspection software that uses AI (deep learning). [Software Used] Software Used: DeepSky

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Inspection of Glossy Workpieces

We will conduct an inspection of "scratches" on glossy doors using AI image inspection software!

The photos you provided made it difficult to conduct inspections due to the shooting environment. While I was able to make a judgment as a simple inspection, it is particularly necessary to carefully consider the lighting, camera, and lens setup when dealing with glossy workpieces. [Inspection Settings and Results] You sent a total of 9 images, of which 6 were used as training images. Each of the 6 images was divided into 60 segments using image segmentation software, and 23 segments with scratches were used for training. The remaining 3 images were used for testing, and these were also divided into 60 segments before inspection. I also tried to detect dents, but I was unable to do so. It is thought that the difficulty in visually identifying certain areas may be the reason for the unsuccessful training, and I believe that defective detection can be achieved depending on the imaging environment. [Software Used] Software used: DeepSky

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Lemon Grade Assessment

We will determine the grade of lemons using AI image inspection software!

In traditional rule-based image inspection, there are cases that are difficult to handle, but with "DeepSky," which uses AI (deep learning), we can apply it to various inspections that were previously impossible. Many people associate visual inspections with small and medium-sized enterprises, but the same applies to large companies, where surprisingly, automation has not progressed in certain areas. While automation in inspection lags behind production, issues such as the aging of inspectors and labor shortages have become problematic. We hope to contribute to efficient production through the automation of inspections. [Inspection Settings and Results] In the verification of lemon grade classification using the provided images, we were able to distinguish four grades with approximately 86% accuracy. Please consider this value as a reference due to the small sample size. We believe that increasing the sample size will lead to improved accuracy. With the version upgrade in 2021, we were able to incorporate a data augmentation feature for training images. This allows for efficient and stable inspections even with a small number of samples. We will continue to add various convenient features and develop user-friendly inspection software from a practical perspective.

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Burrs on Truck Parts

The AI image inspection software detects burrs on truck parts.

We received multiple verification requests from a truck parts manufacturer. In the free sample evaluation, we first conduct a simple verification using the samples or images provided and report the verification results. If requested, we recommend conducting tests (feasibility verification) assuming actual operation, where we 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 use our loaned equipment for verification at your company. [Inspection Settings and Results] We verified whether we could detect burrs using DeepSky with the samples provided. As a result, we detected burrs in 54 out of 55 points, with one point not detected. All other burrs were successfully detected. With DeepSky, we can set the undetected images as additional training images, allowing us to gradually improve accuracy with each occurrence of undetectable defects after operation.

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Detection of Black Spots Including Printed Parts

Detecting the presence of "printing" and "dirt" in the work!

This is a simple evaluation to detect the "black spots" dirt on workpieces, based on inquiries from manufacturers of precision cleaning agents and car chemical products. The priorities are (1) the presence or absence of lot printing, (2) the proper position of lot printing (front and back of the bottle), and (3) the automation of inspection operations for label dirt. Our company generally offers two types of inspection software: the traditional rule-based image inspection "EasyInspector" and the AI-based "DeepSky." This time, we will use DeepSky, which specializes in detecting black spot-like dirt without fixed positioning, assessing the entire screen. 【Inspection Settings and Results】 As a result, we were able to detect the "presence or absence of printing" and "dirt." We registered multiple images (5 images during verification) of the "dirt" area using the inspection software "DeepSky," performed "annotation," and conducted "learning." Since there was only one type of dirt sample, we reproduced dirt in one additional location (by applying transparent tape and recreating black spots on top of it).

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Appearance Inspection of Shumai

We will conduct appearance inspection of shumai using AI image inspection software.

This is an inquiry regarding the appearance inspection of shumai from a food manufacturer. The food industry has many products with irregular shapes, making it difficult to conduct appearance inspections using traditional rule-based methods. Recently, the number of inspection software using AI (deep learning) has increased, and there are more case studies in this industry. Inspection settings and results For the 13 patterns of samples we received this time, it was possible to distinguish between OK and NG using DeepSky. An overview of the judgment results is provided below. Since there was a concern that the sample products might deteriorate over time, we captured images with a camera of approximately 5 million pixels in advance, and during the actual verification, we downsized those images to about 1.3 million pixels for image processing. The field of view and WD (the distance from the inspection object to the lens tip) for actual implementation will be considered separately. Our inspection software has a track record of over 2,000 image inspections. We have numerous examples of inspections for metals, plastics, food, electronic substrates, pharmaceuticals, and more, so please check our website to see if there are similar cases to the inspections you are considering.

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Sample Book Evaluation

The AI image inspection software detects and determines the types of wood grain in the sample book!

The manufacturer that produces various sample books has also considered our inspection software. In cases like wood grain patterns, there are often discrepancies in color, texture, and design, making it difficult for traditional rule-based image inspection methods. Our website offers a free web trial of the inspection software. You can experience the inspection software that uses AI (deep learning) and make inquiries based on that experience. [Inspection Settings and Results] The images are annotations. By using software that employs AI (Deep Learning) for inspection and training it on the areas to be detected, the software itself adjusts its setting parameters and grows to recognize them. As a result of testing with the samples you provided, there were some misjudgments, but we achieved 100% detection with the trained images. Since the sample products used as teacher images were only three, there were instances of false detection for "similar colors" and "variations in wood grain." By increasing the number of teacher images, it is possible to learn various patterns of wood grain (features) and improve the accuracy rate. Additionally, capturing images separately from the left and right (with two cameras) and enlarging them is also an effective method.

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Foreign Objects in Beef Hide Food Products

We will detect whether there are three types of foreign substances in food products made from cowhide!

This is an inquiry from a food manufacturer regarding whether there are any foreign substances in food products made from beef hide. We tested the provided images to see if we could detect three types of foreign substances: "corn," "straw," and "cow hair," using a simple inspection. Our inspection software has a track record of over 2,000 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 the verification conducted with the images you sent, it was possible to detect "corn," "straw," and "cow hair." However, it is necessary to conduct sufficient verification in anticipation of smaller detection items or different patterns (features) that may arise. We determined that detection would be difficult with full-width images due to the small size of the detection targets. This time, we conducted the verification with half-width images. We labeled the annotations as "corn" for corn, "straw" for straw, and "cow hair" for cow hair. We trained the model using 90 annotated images as teacher images. It was set up so that if any one of corn, straw, or cow hair was detected, it would be deemed a failure.

Company：スカイロジック
Price：Other

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[AI Image Inspection Case] Cabbage Appearance Inspection

We will conduct an appearance inspection of cabbage using AI image inspection software.

In food manufacturing, irregularly shaped work (ingredients) is commonplace, and even minor defects or foreign matter can lead to significant accidents. This time, we have received a request for a simple inspection of food. The ongoing societal issues of a declining workforce and the aging of skilled workers show no signs of stopping, and efforts to support productivity improvement are increasingly in demand. Image inspection technology has gradually become more sophisticated. Please consider the introduction of image inspection to maintain the Japanese quality that allows us to consume safe and secure food. 【Inspection Settings and Results】 This is a verification based on the images provided. The areas of interest to be detected were enclosed in frames and labeled by type. This time, we annotated the images into four categories: "discoloration," "flower buds," "length," and "length 2." Since the verification was conducted by dividing the existing images into labeled and unlabeled data, the accuracy was not very high, but it can be inferred that increasing the sample size will improve accuracy to some extent. 【Software Used】 Software used: DeepSky Learning Edition

Company：スカイロジック
Price：Other

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Visual inspection software Product List and Ranking from 21 Manufacturers, Suppliers and Companies

Visual inspection software Manufacturer, Suppliers and Company Rankings

Visual inspection software Product ranking