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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

316～330 item / All 470 items

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[AI Image Inspection Case] Support for Dimensional Inspection of Metal Products

[AI Image Inspection Case] Support for Dimensional Inspection of Metal Products

We provide support for the dimensional inspection and operation of metal products!

This is an inquiry from an industrial equipment manufacturer with whom we have previously done business. EasyInspector can be used for various parts and defects, but the accuracy of detection can vary depending on the settings. Although it is a one-time purchase inspection software, we will continue to provide support after implementation. 【Inspection Settings and Results】 Regarding the method of inspection using "color presence inspection," in this case of the reverse image, it is likely that detection was possible because it happened to be slightly elevated. Therefore, we would like to put the inspection method for "color presence inspection" on hold and ask you to consider the inspection method using "dimensional angle inspection." Even in the case of "dimensional angle inspection," if the position of the inspection item is elevated (upward on the screen), it may not detect the correct angle. An effective method is to add another inspection frame, set up color inspection and other settings there, and check whether the inspection item is not elevated too much.

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Presence or Absence of Burrs in Holes

[AI Image Inspection Case] Inspection of Presence or Absence of Burrs in Holes

We will inspect the presence or absence of burrs in metal parts processed by pressing using AI image inspection software!

This is an inquiry from a customer who is contacting us for the first time regarding the detection of irregular shapes such as elongated holes and round holes due to deformation or foreign material adhesion in metal parts with hole diameters ranging from φ2.0 to 6.5mm. The target products are between L70 and 200mm. Upon further discussion, it was revealed that the purpose was to detect burrs in the holes. They have sent us samples for a simple verification. 【Inspection Setup and Results】 We captured images of the surface where burrs occur (the surface marked with a marker) facing the camera. It is desirable to set up the inspection surface parallel to the camera sensor. While the software has a function to correct misalignment, we believe that fixing the workpiece with an L-shaped jig or similar will allow for more accurate inspections.

  • Image Processing Software

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[AI Image Inspection Case] Presence or Absence of Cushioning Sponge

[AI Image Inspection Case] Presence or Absence of Cushioning Sponge

We will check for the presence of cushioning sponge and plastic parts!

We will inspect the cushioning foam attached to the Styrofoam used for packaging the product. This inquiry comes from a manufacturer of woodworking and plastic processing machinery. They consulted us regarding the inspection of the presence or absence of gray and black cushioning foam attached to the front and back surfaces, stating that positional accuracy is not currently necessary. They would like to determine whether the foam is partially present or absent and whether it is applied overall. 【Inspection Setup and Results】 We conducted the inspection of the presence or absence of the cushioning foam using the sample images you provided. By using EasyInspector's "Presence of Specified Color Inspection," we were able to determine the presence of the cushioning foam. Additionally, we could also determine the presence of plastic parts using the same function. Since you provided images of the parts installed (front and back), we created test images in a paint software showing a state where some parts are missing (NG state) for verification. The left image shows the inspection frame, and the right image shows the detection frame.

  • Image Processing Software

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[AI Image Inspection Case] Surface Scratch Inspection of Resin Products

[AI Image Inspection Case] Surface Scratch Inspection of Resin Products

We will conduct surface scratch detection on resin products using AI image inspection software.

This is an inquiry from a sports equipment manufacturer. We will conduct surface scratch detection on resin products. Our company accepts 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】 An inspection frame was installed at one location on the inspection surface, and settings were made using EasyInspector's "Scratch Detection" function. Detection was possible in 0.47 seconds. The lighting was set to make scratches appear black on the surface, and each value was adjusted to detect the black areas. Since no scratches exceeding the set values were detected in good products, they were deemed "pass." In defective products, the scratched areas appeared black and exceeded the set values, resulting in a "fail" judgment. The surface of the NG products appeared brighter around the scratches, making the scratched areas stand out more. However, if they had appeared with brightness similar to that of good products, it would have been necessary to adjust the detection sensitivity. In that case, sensitivity would be adjusted to a higher setting, which may increase the likelihood of false judgments. The installation position of the inspection items was set without "shift correction" on the premise that it would be fixed in the same position each time.

  • Image Processing Software

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[AI Image Inspection Case] Defect Inspection on Plastic Product Trays

[AI Image Inspection Case] Defect Inspection on Plastic Product Trays

We will conduct a missing parts (counting) inspection of the components in the tray using AI image inspection software!

Due to the burden of counting small parts at a plastic parts manufacturer, we are requesting verification for automation of the counting process. This falls within the expertise of EasyInspector. A simple verification can be conducted by sending images. 【Inspection Settings and Results】 By using EasyInspector's "Presence of Specified Color Inspection" feature, we were able to inspect parts in 24 trays in 0.93 seconds. The left image shows the inspection settings. The right image indicates a misjudgment of the background color in the detection frame. Although you sent a 5-megapixel image, detection is possible even with a camera of about 1.3 megapixels for this type of inspection; reducing the pixel count will shorten the inspection time. If it is not necessary to identify (record) the locations of missing items, we can enclose the entire set (24 workpieces) within a single inspection frame. If the background of the inspected items is a transparent tray, placing a white paper underneath will enhance the contrast between the workpieces and the background, allowing for more stable and accurate detection in a shorter time. In the inspection settings, the report is configured with "Automatic/Default" for offset correction. If stable positioning can be achieved with jigs or similar tools, setting the offset correction to "None" will further reduce the cycle time.

  • Image Processing Software

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[AI Image Inspection Case] Determining Defects in Rollers

[AI Image Inspection Case] Determining Defects in Rollers

We will conduct a simple verification of the interruption of the liquid film on the roller using AI image inspection software.

The rollers of the printing manufacturer are rotating at high speed. If a defect occurs, it can continue to appear, and inspection can be performed with our image inspection software. This time, we will conduct a simple verification of the interruption of the liquid film. 【Inspection Settings and Results】 By using the "Scratch and Defect Inspection" feature of EasyInspector, it was possible to detect the interruption of the adhesive liquid film from the entire screen in 0.19 seconds. It can be considered that inspection can be performed with a frame capture every 0.19 seconds, but the inspection cycle may vary depending on the specifications of the computer and the settings of the inspection frame. 【Software Used】 Software used: EasyInspector310 (formerly EasyInspector) The current 'EasyInspector2' color package can be used for inspection【Scratch and Defect Detection】.

  • Image Processing Software

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[AI Image Inspection Case] Detection of Scratches on Delicate Parts

[AI Image Inspection Case] Detection of Scratches on Delicate Parts

Detect scratches, abrasions, and dirt on delicate parts using AI image inspection software!

In manufacturers that handle delicate parts, defects such as scratches have led to accidents. Ideally, we would conduct a more reliable verification by sending sample products for testing, but our company also accepts numerous verifications using sample images. 【Inspection Settings and Results】 By using the "Scratch Detection" feature of EasyInspector, we were able to detect only the dark black areas from the entire screen at one location. In the left image, there are parts that are not scratches but have been detected. Since this area appears darker compared to other smudges and dirt, if we increase the sensitivity of the settings to the level that can detect the scratches in the right image, it will also incorrectly identify the acceptable areas in the left image as defects. 【Software Used】 Software used: EasyInspector710 The current 'EasyInspector2' color package [Scratch and Object Detection] can be used for inspections.

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Labels and Leads

[AI Image Inspection Case] Inspection of Labels and Leads

We will verify the incorrect insertion of lead wires and the presence of labels using AI image inspection software!

This was the first inquiry from a customer primarily manufacturing lighting fixtures, and they are considering the use of image inspection to verify the quality of assembly. Given the current social situation, we are also accommodating meetings via web conferencing. Unless specified otherwise, we often send invitations using "Microsoft Teams," but we can accommodate any common applications such as Zoom or Skype. After understanding the overview of our systems and software, it is common for us to receive sample submissions. 【Inspection Setup and Results】 We conducted verification of incorrect lead wire connections and the presence of labels using the samples we received. By utilizing EasyInspector's "Color Comparison Inspection Function," it was possible to determine good and defective products. The inspection was completed in 2.49 seconds per inspection frame across 11 inspection frames. After reporting the simple verification, we may receive requests for specific paid verification for operational purposes. Additionally, we can lend demo units to customers free of charge for them to experience the product firsthand.

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[AI Image Inspection Case] Detection of Dents and Seat Posts

[AI Image Inspection Case] Detection of Dents and Seat Posts

Detecting dents and seat posts with AI image inspection software!

We have received a request for a simple verification of a new project from an overseas audio equipment manufacturer with whom we have previously done business. Since the release of our inspection software "DeepSky," which utilizes AI (deep learning), we have received various inquiries. Even major companies surprisingly have not fully automated their inspection processes. Automation is being promoted with the goals of pursuing stable inspection accuracy and reducing costs. As the shortage of personnel due to the aging workforce becomes more pronounced, we encourage you to consider improving operational efficiency through image inspection to protect "Made in Japan" manufacturing. 【Inspection Settings and Results】 We tried annotating the entire image you sent, but we were unable to identify it as expected, resulting in similar outcomes to what you verified. Even using a method that only trains on OK samples, nearly 90% of the NG samples were classified as OK. However, by assuming a method of determining based on the number of detections within a designated area, as proposed yesterday, and training on the sheet post and recess (5 teacher images), we found that we could achieve 100% correct detection with the images you provided this time. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Detection of Cracks in Parts

[AI Image Inspection Case] Detection of Cracks in Parts

We will conduct crack inspection of parts using AI image inspection software!

We received a request for a simple evaluation regarding the inspection of cracks in parts from an audio equipment manufacturer we have been working with for some time. We received 16 sample images. Our company accepts 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】 Out of the 16 images, 10 were used as training images. We inspected a total of 16 images, consisting of 10 training images and 6 untrained images (3 OK and 3 NG). We set the criteria to "pass" when there are 0 cracks detected and "fail" when 1 or more are detected, and we were able to detect all 16 images. Even with unknown images that were not trained, we were able to detect them as cracks. The image on the left shows the number of detected cracks displayed on the screen. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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【AI画像検査事例】板金加工品の検査

【AI画像検査事例】板金加工品の検査

板金加工品のさまざまな不良を検出し、対象の箇所に合わせた検査を行います！

精密板金のメーカーでは製品や部品の検査は欠かせません。今回は「曲げ未加工」「逆曲げ」「曲げ寸法違い」「ボール盤未加工」「BR未加工」「圧入ナット有無」「圧入ナットズレ」についての評価依頼です。 高いクオリティを維持するために弊社の検査ソフトをご検討いただいています。 【検査設定と検査結果】 EasyInspectorの「マスター画像との比較・指定色の有無検査」機能を使用することにより良品と不良の違いを検出し、見た目の似通った類似品（異品）を1秒未満で判定することができました。 EasyInspectorでは様々な検査を１回のトリガーで一度に実行できる魅力があります。 無料サンプル評価では、まず頂いたサンプルまたは画像を使用して簡易検証を行い、検証結果を報告いたします。 簡易検証ではご要望の検出/判定が可能か社内の機器で評価します。簡易検証で検出や判定できた場合、実際の運用を想定したテスト（実現性検証）を行い、処理時間や判定精度などの評価を行うことをお勧めしています。実現性検証を行う場合、弊社で引き続き行う(有償)か、弊社貸出機を使って御社で検証されるかお選び頂けます。

  • Image Processing Software

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[AI Image Inspection Case] Inspection of Metal Rivet Lifting

[AI Image Inspection Case] Inspection of Metal Rivet Lifting

Detect and evaluate the peeling of rivets using AI image inspection software!

This is a simple verification using sample images at the request of an industrial machinery manufacturer. When introducing image inspection, the shooting environment for the inspection area becomes very important. Recently, there have been many cases where it is difficult to send sample products due to compliance and security reasons. While we can accept verification through image submission, there may be instances where we cannot provide guidance for a better imaging environment. In such cases, we may suggest creating a model in-house to conduct the verification, even though the texture of the sample may change. 【Inspection Settings and Results】 When learning with defective images that have significant differences compared to good products as training data, it was possible to detect those with large differences as defects. However, there were some cases where it was difficult to detect those with small differences. Regarding image acquisition, we determined that detection would be difficult if imaging was performed at a free distance for the current inspection item. If the imaging environment is adjusted so that the rivet appears in the center of the image at the same size, there is a possibility that detection accuracy could improve. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Detection of Dents and Scratches on Metal Products

[AI Image Inspection Case] Detection of Dents and Scratches on Metal Products

The AI image inspection software detects dents and scratches on metal products!

We conducted a simple verification of dents and scratches on metal products such as motorcycle and automobile parts. Due to their almost identical appearance, there were incidents of shipping similar defective products, which caused issues. In the inspection process, automation is being promoted with the aim of pursuing stable inspection accuracy and cost reduction. As the shortage of personnel due to the aging workforce becomes more pronounced, we encourage you to consider improving operational efficiency through image inspection to protect "Made in Japan" manufacturing. 【Inspection Settings and Results】 Using the samples we have on hand for verification, we were able to detect dents and scratches. However, small dents and scratches present on good products, or surface conditions that appear as such in images, were detected as abnormalities. For extremely small items, we believe there are ways to address this by categorizing detection targets into "large dents" and "small dents," and only marking it as NG when a "large dent" is found.

  • Image Processing Software

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[AI Image Inspection Case] Solder Acceptability

[AI Image Inspection Case] Solder Acceptability

The AI image inspection software determines the acceptability of the solder on the cable.

We received a request for a simple evaluation from a manufacturer specializing in wireless power supply and charging products, which we have previously contacted. Visual inspection is often seen as a simple task, but it requires the skill to determine "OK" or "NG," and it may not be a task that everyone can perform consistently. Factors such as physical condition and prolonged working hours can lead to fatigue, which is one of the causes of unstable inspection results. Our company is also developing inspection software that can identify fine scratches and dirt, enabling judgment similar to that of skilled workers. 【Inspection Settings and Results】 We tested with the 10 images provided. The images were assigned as follows: 1-3 → Test images, 4-6 → OK training data, N7-N10 → NG training data Although we could not secure enough NG test images due to the small number, we conducted training with 7 images and were able to correctly classify the 10 images. 【Software Used】 Software used: DeepSky

  • Image Processing Software

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[AI Image Inspection Case] Short Inspection of Plastic Molded Products

[AI Image Inspection Case] Short Inspection of Plastic Molded Products

Detect subtle shorts in plastic molded products with AI image inspection software!

The samples you sent this time included a clear significant short and a subtle short that is not easily noticeable at first glance. It was confirmed that even minor shorts exhibited surface roughness associated with the short. With the inspection using EasyInspector, it would be easier to report if you could send us a "master image (good product)" and a "defective product that seems the most difficult to detect." 【Inspection Settings and Results】 Using EasyInspector's "color comparison inspection - comparison with master image" inspection function, we were able to conduct the inspection in 0.45 seconds. Even subtle shorts that were close in shape to the master image were detected for surface roughness (reflected white) and judged as unacceptable. The arrow markings in the photo are also white, but this time they were excluded from detection due to the non-detection pixel settings.

  • Image Processing Software

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