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

Last Updated： Aggregation Period:Nov 05, 2025～Dec 02, 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 05, 2025～Dec 02, 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. 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 05, 2025～Dec 02, 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] Detection of Surface Defects (Chatter Marks) on Metal スカイロジック

Visual inspection software Product List

31～45 item / All 470 items

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[Technical Support] Would you like to try AI in food inspection?

[Technical Support] Would you like to try AI in food inspection?

It can be used for food inspections such as material defects, foreign matter contamination, insufficient quantity, and missing components!

Until now, Skylogic has primarily handled image inspection projects focused on industrial products. This is because the nature of industrial products, which involves "mass-producing the same item," matched well with conventional (rule-based) image processing methods. Conversely, it has been difficult to handle items with unstable colors and shapes, even among industrial products, using traditional methods. However, with the emergence of image processing methods utilizing AI (deep learning), items with variability can now be included in the scope of image processing. This is because AI excels at "detecting only what we want to find while ignoring trivial changes." Now, connecting this to the title, when we talk about "items with unstable colors and shapes," we are referring to food products. Issues such as material defects, foreign object contamination, quantity defects, and missing components... even when the subject is food, products produced in factories are likely to face similar challenges as industrial products. However, the difficulty of processing with traditional methods has led to these issues being abandoned. (And I have also given up on them.) Why not try AI?

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Technical Support: Discovering Anisakis with AI

Technical Support: Discovering Anisakis with AI

Skylogic has already developed AI for detecting foreign substances in food.

▼Obtaining Anisakis → Until Filming This time, we conducted a verification at our company to find "Anisakis," which we received several inquiries about from customers, using AI. We started by trying to obtain live Anisakis, but it turned out to be more difficult than we expected... We visited several supermarkets and fish shops in the city and made phone calls to request their help. Despite it being an extremely busy time of year, I made a strange request to unknown housewives, saying, "Can you give me live Anisakis for an experiment to find Anisakis with AI (to summarize)?" I am very grateful to the supermarkets that cooperated with us, and to the staff in the fresh fish section who helped us. Thank you very much. We assembled the equipment assuming an operation where we conduct AI Anisakis inspections on a slowly moving conveyor, and if detected, a buzzer would sound to stop the conveyor. Anisakis glows in response to specific wavelengths of ultraviolet light, so we use UV LED light sources and filters that pass specific wavelengths. In image processing, it is also important to capture images that make it easier to find Anisakis through optical manipulation.

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Knowledge of Visual Inspection: Principles of Object Recognition (CNN Edition)

Knowledge of Visual Inspection: Principles of Object Recognition (CNN Edition)

I will explain the principles of object recognition!

Recently, we have been receiving more inquiries from customers using AI software (such as EasyInspector2 and DeepSky) asking questions like, "Why does this happen?", "Isn't it supposed to be like this?", and "What is going on inside?". In such cases, it seems that the person in charge is unsure about how to explain things. Indeed, when trying to provide an intuitive and easy-to-understand explanation, they often end up relying on analogies, or if they attempt to explain in detail, they find that specialized books provide more thorough information. I have also been unable to find suitable explanatory materials for customers who want to take a deeper dive into the principles of AI image processing.

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[Inspection Technique] Regarding PC Selection When Connecting Multiple Cameras

[Inspection Technique] Regarding PC Selection When Connecting Multiple Cameras

We will introduce the selection of PCs when connecting multiple cameras using the AI features of EasyInspector2!

We sometimes receive inquiries from customers regarding the specifications of PCs when connecting multiple devices. This time, we would like to introduce the behavior when connecting multiple cameras using the AI function of EasyInspector2 (hereinafter referred to as EI2). We hope this will serve as a reference when selecting a PC. Additionally, we hope it will also provide guidance on the expected increase in inspection time when connecting multiple devices. We used a multi-controller (hereinafter referred to as EIMC) to start and inspect six EI2 units. ■PC ■Hardware Configuration ■Software Configuration ■Results *For more details, please refer to the related links (blog).

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Knowledge of Visual Inspection: The Concept of Resolution in Deep Learning

Knowledge of Visual Inspection: The Concept of Resolution in Deep Learning

I will introduce the perspective on resolution in deep learning!

We often receive requests to use high-resolution cameras to improve detection capability. In the case of rule-based image processing, using high-resolution images tends to improve resolution and enhance detection capability, but this is not always the case with deep learning. Below is a brief explanation of the perspective on resolution in deep learning, albeit in a rough manner. Let's consider images like (1) to (3) in Figure 1. (1) Total area 10×10, area of the gray rectangle 4 (2) Total area 20×20, area of the gray rectangle 16 (3) Total area 10×10, area of the gray rectangle 16 The area of the gray rectangle in (2) is four times larger than in (1), but when looking at the ratio of the gray rectangle to the total area, (1) is 4/100 and (2) is 16/400, both representing only 4%. In terms of "ease of detecting the gray rectangle" in deep learning, (1) and (2) are almost the same.

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Knowledge of Visual Inspection: Differences in Learning and Inspection Speed with GPU

Knowledge of Visual Inspection: Differences in Learning and Inspection Speed with GPU

We will examine the differences in learning and testing speed using GPUs!

This time, I would like to verify the "differences in learning and inspection speed using GPUs" as stated in the title. I will compare the differences in learning and inspection times with two types of GPUs and 16GB and 8GB of RAM. ■Conditions ■Verification Configuration ■Results *For more details, please see the related link (blog).

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Knowledge of visual inspection: AI requires appropriate teacher images and correct annotations.

Knowledge of visual inspection: AI requires appropriate teacher images and correct annotations.

I will introduce "teacher images" and "annotations"!

This is something that our sales engineers explain almost every time they talk to customers, but despite being a crucial factor that greatly influences AI performance, many of the discussions found online are too general to be practically useful. We thought that if we could provide a more practical, specific, and realistic explanation, it would surely be helpful to everyone, which is the background of this blog. ■ No matter how good the AI is, if it is taught incorrectly, it will produce incorrect results - About teacher images - About annotations - Convenient features in annotations - Blogs and videos that are helpful *For more details, please see the related links (blog).

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Monitoring in other industries such as agriculture too! 'EasyMonitoring2'

Monitoring in other industries such as agriculture too! 'EasyMonitoring2'

The AI automatic meter monitoring system monitors people, animals, various monitored objects, locations, and situations!

Until now, the focus has primarily been on the utilization of equipment within factories and manufacturing sites, but with AI capabilities, it has become possible to detect humans and animals, allowing for applications in various industries such as agriculture. This contributes to reducing time and effort through verification. ■ Management of control panels in greenhouses Automates visual patrol checks using cameras and image processing. ■ Measures against wildlife damage Ignores humans and vehicles, detecting only birds and animals. ▼ Image collection possible in areas without power In forested areas and farmland without power, it is possible to supply power using solar batteries and collect images via mobile phone lines. Real-time notifications can be sent regarding damage from wild boars and deer, as well as illegal dumping. 【Functionality and monitoring examples】 Analog meter reading Reading flow meters, float meters, etc. Digital display (7-segment) numerical reading Lamp status (such as in control panels) Dot matrix numerical reading Reading values and displays from centralized management PC monitors Reading vertical rotation power meters and difficult-to-read characters Monitoring absence of people and entry into restricted areas Detection of insect contamination and animal intrusion Condition of liquids

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【物流・建設分野にもAIを】デジタル化により作業量を大幅に削減

【物流・建設分野にもAIを】デジタル化により作業量を大幅に削減

画像処理による箱のサイズ測定、建設現場におけるメーター監視、スマホとQRコードを使用した資材管理などを行います。

物流の分野では箱の重量やサイズを測定したり、搬入した資材の場所を記録・共有したりする場面があります。建設では資材が到着しているか、敷地のどの場所に何の資材があるかを共有したり、鉄筋やパイプの数を数えたりする必要があります。今後ニーズがさらに高まっていく物流や建設の分野で、少ない人員でより安全・効率的に作業を行うことは喫緊の課題となっています。 ▼ＡＩとＱＲコードで作業をデジタル化 スカイロジックではスマホとＡＩで撮影された物体の個数、本数を一瞬で計数する「cazoeTell（カゾエテル）」、部品・物体を区別して計数する「wakeTell（ワケテル）」、スマホとQRコードで資材の場所を共有する「QRものドコ」、ネットワークカメラとQRコードで資材の場所を特定する「QR工程管理」、箱のＷＤＨサイズと重量を瞬時に計測する「BoxMetrix」、建設現場のメーターを監視する「EMCloud」など、物流、建設における効率化のための様々な製品を開発しています。 ※ 「QRコード」は株式会社デンソーウェーブの登録商標です。

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[AI Image Inspection Case] Verification of Print Defects

[AI Image Inspection Case] Verification of Print Defects

We will verify the printing defects using AI image inspection software!

We received an inquiry from a company that produces labels and POP materials, and we will verify the printing defects. First, we asked them to visit our company for an explanation of our inspection software, leading to a simplified verification process. Our company also accommodates web meetings for those who cannot visit in person. To prevent the work's backing from lifting and affecting the inspection, we pressed down on it with a transparent glass plate while taking photos. If we took photos with the backing still lifted, we detected many discrepancies compared to the master. We covered the "comma" in the print with black to eliminate it and used the "comparison with master" feature of EasyInspector when it reappeared for verification. As a result, we were able to detect the comma. Since the detected value was less than 10 pixels, which is very small, if we want to detect such defects, we need to be careful about the position of the sample when placing it (to minimize misalignment) and avoid detecting discrepancies due to changes in lighting.

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[AI Image Inspection Case] Liquid State Inspection

[AI Image Inspection Case] Liquid State Inspection

We will detect the state of the liquid (whether the wastewater treatment is sufficient) using AI image inspection!

We received an inquiry from an industrial machinery manufacturer regarding wastewater treatment. We will inspect whether the treatment has been sufficiently completed. At SkyLogic, we research the latest technologies such as AI and IoT, transforming them into user-friendly products for our customers. For example, in the AI field, we offer DeepSky, which enables flexible recognition through deep learning, and in the IoT field, we have EasyMonitoring, which processes images from dozens of network cameras. By using the "Defect Inspection" feature of EasyInspector, we detect the processing status of the entire image at one location. When inspecting with EasyInspector, it is necessary to stabilize the imaging environment. Although we were able to detect it this time, we recommend using inspection software that employs AI (deep learning) for operational purposes. [Software Used] Software: EasyInspector310 (formerly EasyInspector) Current 'EasyInspector2' color package [Defect Detection] Inspections can be conducted with 'EasyMonitoring2'.

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[AI Image Inspection Case] Reading of Device Panel

[AI Image Inspection Case] Reading of Device Panel

Detecting the presence or absence of reading characters and lighting for the seven search items displayed on the panel installed in the device.

This is a request to conduct a simple verification of the reading from the panel located at the front of the device. We received an inquiry via email from a container manufacturer. They sent images to confirm the reading of seven search items displayed on the panel attached to the device, as well as the presence or absence of illumination. Reading such device panels can be considered a common case across various industries. Our inspection software can be of assistance in many different locations. 【Inspection Settings and Results】 By using the "OCR Pro" function of EasyInspector, we verified the reading of five displayed items. In the left image, each character was recognized individually, and it was read as "25." In the right image, the character spacing is narrow, making it difficult to recognize individual characters in the current display. There are various methods for determining illumination, but here we set it so that if the detection amount of gray is above the reference value, it is considered a pass; if it does not meet the reference value, it is considered a fail.

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[AI Image Inspection Case] Counting Cells of Parts

[AI Image Inspection Case] Counting Cells of Parts

We will detect the count of cells for the parts in the image!

This is a verification request from a parts manufacturer. Our company has various industrial machinery and control equipment manufacturers as clients in Hamamatsu City, Shizuoka Prefecture, where the manufacturing industry is thriving. We have a track record of providing support for over 1,000 cases and have developed inspection software that is easy to integrate and sold as a one-time purchase, which we believe leads to repeat business. 【Inspection Settings and Results】 The cell count detection in the left image is the result of counting cells using EasyInspector (traditional rule-based method). It compares how many clusters of the same color can be detected within a fixed-size inspection frame. 【Software Used】 Software used: EasyInspector (formerly EasyInspector) The current 'EasyInspector2' color package can perform inspections based on the presence or absence of specified colors.

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[AI Image Inspection Case] Measurement of Heat Shrinked Transparent Film

[AI Image Inspection Case] Measurement of Heat Shrinked Transparent Film

We will measure the heat-shrinked transparent film using AI image inspection!

There was a request to measure the red frame area of the magic using heat-shrinkable transparent film. The manufacturer of chemical products and plastic processing for this simple verification is a repeat customer who has been inquiring for some time. 【Inspection Settings and Results】 In the image example (Figure 2), the lengths from each opposing frame to the frame are being measured. Since the number of frames can be increased up to 999, measuring arbitrary points vertically and horizontally and calculating the average value should improve accuracy compared to measuring only four points. The measurement values are calculated based on pixel counts, and by multiplying this by a coefficient that indicates how many millimeters one pixel represents, the actual dimensions can be converted.

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[AI Image Inspection Case] Missed Inspection of Label Instructions

[AI Image Inspection Case] Missed Inspection of Label Instructions

Inspection of 32 label inspection items covered by AI image inspection software!

A customer, a high-performance plastic resin manufacturer, contacted us through our website regarding their concerns about the user manual inspection. They mentioned that due to the large amount of text and small font size, they are experiencing missed inspections and longer inspection times. 【Inspection Settings and Results】 By using the "Comparison with Master Image" feature of EasyInspector, we were able to cover 32 inspection items for the labels and make determinations. The inspection time during verification was approximately 15 seconds, but when we set up an overall inspection frame and performed more detailed "shift correction," it took about 2 minutes. 【Software and Equipment Used】 Software Used: EasyInspector (formerly EasyInspector) Field of View: Approximately 622 x 455mm Minimum Size of Inspection Target: 2mm Number of Inspection Points: 8 Camera Resolution: 14 million pixels Lens Focal Length: 12mm Distance from Lens to Product: Approximately 540mm Lighting: Two bar lights Distance from Lighting to Inspection Item: Illuminated from about 100mm above the left and right sides The current 'EasyInspector2' color package can perform inspections using the "Comparison with Master Image" feature.

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