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Image Inspection System - メーカー・企業59社の製品一覧とランキング

更新日: 集計期間:Oct 29, 2025~Nov 25, 2025
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Image Inspection Systemのメーカー・企業ランキング

更新日: 集計期間:Oct 29, 2025~Nov 25, 2025
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  1. 山善 Osaka//Trading company/Wholesale
  2. シリウスビジョン(旧ナビタスビジョン) Kanagawa//Testing, Analysis and Measurement
  3. ブル精密 (株)ブル精密 本社 Tochigi//Glass and clay products
  4. 4 ミラック光学 Tokyo//Testing, Analysis and Measurement
  5. 5 Kyoritsu Electric Corporation Shizuoka//Industrial Electrical Equipment

Image Inspection Systemの製品ランキング

更新日: 集計期間:Oct 29, 2025~Nov 25, 2025
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  1. AI Image Inspection Device "EYEbeGenesis" 山善
  2. [Case Study] One-Shot 3D Shape Measuring Instrument 'VR-3200' ブル精密 (株)ブル精密 本社
  3. Artificial Intelligence Image Inspection System "AI Hayabusa" *Case study collection available as a gift. ミラック光学
  4. 4 [Work Support System] Image Poka-Yoke System Kyoritsu Electric Corporation
  5. 5 Image analysis tool 'Open eVision' *Free trial currently available Euresys Japan ユレシス ジャパン

Image Inspection Systemの製品一覧

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[Video Available] "Roxy AI" cleans data and improves accuracy!

Garbage In, Garbage Out. An AI that has learned from incorrect data is useless.

There is an explanatory video, so please take a look. Garbage In, Garbage Out. An AI that has learned from incorrect data is useless. In fact, AI developers say they spend 80% of their time on data preparation. Can using the latest research results and the strongest algorithms solve the problem? No. Underestimating data will render even the strongest AI a wasted treasure. If you allow difficult-to-find defects mixed in with normal images to be learned, the result will be an AI that does not detect features similar to the mixed-in defects, and that is not the AI you expected. What a human can intuitively understand, AI cannot adapt to. So, should we thoroughly check all normal images to ensure that defects are definitely not present? That would be ideal, but it requires an enormous amount of time and attention. Roxy AI considers that inappropriate data is something that will mix in. Inappropriate data that has mixed in can be removed and cleaned up. With Roxy AI, data can be cleaned throughout the entire AI learning process. We clean the data and create conditions that make it easier for the AI to learn.

  • Visual Inspection Equipment

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Exhibition at FOOMA 2022: AI Image Judgment Solution Y's-Eye

Make visual inspections easier! No knowledge of AI or image processing required. An AI image judgment (automated quality inspection) system that can determine quality in real-time on the manufacturing floor.

Y's-Eye is an AI image judgment service that supports the stabilization and automation of inspection quality. It can also be integrated with elimination mechanisms to eliminate defective products, enabling further automation. It is utilized in various quality inspections required by the HACCP system. By accumulating data, it also contributes to traceability.

  • Other food machinery

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Inspection Target Specialized Model: Y's-Eye Hamburger

The first specialized model for inspection targets of the AI image judgment solution "Y's-Eye," which supports the stabilization and labor-saving of inspection quality.

What is the inspection-target specialized model "Y's-Eye Hamburger"? It is the first model of the AI image inspection solution "Y's-Eye," which combines AI image inspection with an exclusion mechanism to support the stabilization of inspection quality and labor-saving. This model specializes in inspecting hamburger patties and has been packaged to enable a smoother implementation in a shorter time than before. By consolidating the technology of the Yaskawa Group and achieving automation of the inspection process through the collaboration of AI and robots, it is expected to contribute to improving yield rates, addressing labor shortages, and reducing food waste by analyzing defect trends from inspection result data. This solution is attracting attention in the food manufacturing industry, where the criteria for shape and passing products are not uniform, and reliance on manual inspection has been necessary.

  • Inspection robot

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Artificial Intelligence Image Inspection System "AI Hayabusa" *Case study collection available as a gift.

By equipping X-ray inspection devices and multi-joint robots, they become the brains of machines. We are also presenting examples of "AI-equipped robots" as appearance inspection devices!

"AI Hayabusa" is an "artificial intelligence image inspection system" that overcomes the weaknesses of conventional image inspection tools, which were primarily based on pattern matching. The appearance inspection robot "AI Robot," equipped with "AI Hayabusa," leverages the features of artificial intelligence and image processing to inspect various surface defects such as scratches, dents, bumps, fluff, and color unevenness that appear on inspection objects with curved or mirror surfaces. [Features] - Inspection using artificial intelligence + image processing - Supports online, offline, and embedded device applications - Image processing system for flat measurement, blob analysis/morphology, character recognition/verification, matching, and machine vision (shape identification) - Functions for 3D shape measurement, automatic classification, and barcode reading - Achieves many benefits, including a reduction in personnel to the level of skilled workers *We are currently offering a collection of case studies and product materials that introduce examples of defect judgments. You can view them via "PDF Download." Please feel free to contact us with any inquiries.

  • Visual Inspection Equipment

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psmerge

Concatenation of PS files

Concatenation of PostScript files

  • Document and Data Management

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Case study of an image inspection system using AI: Surface inspection process of mirrors.

Inspection accuracy improved from 60% to 99% by developing a system using AI deep learning technology, resulting in a reduction of visual inspection errors.

We would like to introduce a case where we developed a mirror surface inspection system using AI deep learning technology in the product inspection process of Murakami Kaimeido Co., Ltd., which boasts a high market share in rearview mirrors. This development has achieved an improvement in inspection accuracy (from 60% to 99%) and reduced the burden on inspectors who previously conducted visual inspections. 【Problems with the Conventional Detection System】 ■ Difficulty in quantifying image color and other attributes ■ Ambiguity in standards due to human determination of thresholds ■ Inspection accuracy of around 60% ■ Final confirmation by inspectors through visual checks 【Effects After Implementation】 ■ After full-line implementation, the number of inspection workers was reduced by 70% ■ With an eye on overseas expansion, data obtained from the automation of inspections can also be used to optimize upstream processes *For more details, please refer to the PDF document or feel free to contact us.

  • 企業:Rist
  • 価格:Other
  • Other inspection equipment and devices
  • Image Processing Software

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Case Study 1 of AI Image Inspection Service: Development of AI for Diagnosing Exterior Wall Cracks

We will introduce a case where our AI image inspection service has achieved the refinement, standardization, and efficiency of exterior wall crack inspection operations.

Rist has developed a system in collaboration with Tokyu Livable, Inc., a real estate company, and Japan Home Shield, Inc., which specializes in ground surveys and building inspections, to diagnose cracks in the exterior walls and foundation of used houses based on certain standards using AI. (Patent obtained) 【Challenges】 ■ Since the evaluation is based on visual inspection and judgment by inspectors (humans), there can be variations in the assessment. ■ To ensure inspection accuracy, double-checking is conducted in the back office. ■ To maintain inspection quality, it takes several days from on-site inspection to final evaluation. We have resolved these challenges with our AI image inspection system. *For more details, please download the PDF document or feel free to contact us.

  • 企業:Rist
  • 価格:Other
  • Image Processing Software
  • Other inspection equipment and devices

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Case Study 2 of AI Image Inspection Service: Surface Inspection Process of Mirrors

We will introduce a case where our AI image inspection service improved inspection accuracy (from 60% to 97%) and reduced the burden on inspectors through visual inspection.

Rist developed a mirror surface inspection system using Deep Learning technology in the product inspection process of Murakami Kaimeido Co., Ltd., which holds the No. 1 domestic market share in rearview mirrors. This system achieved an improvement in inspection accuracy (from 60% to 97%) and reduced the burden on inspectors who previously relied on visual checks. 【Challenges】 ■ Difficulty in quantifying image color and other characteristics ■ Ambiguity in standards due to human determination of thresholds ■ The need for final visual confirmation by inspectors We have addressed these challenges with our AI image inspection system. *For more details, please download the PDF document or feel free to contact us.*

  • 企業:Rist
  • 価格:Other
  • Image Processing Software
  • Other inspection equipment and devices

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AI Inspection System for Agriculture "AI Hayabusa"

Reduce the burden of visual monitoring! AI instantly identifies pests and diseases.

Detecting pests and diseases in crops is a task that requires skilled techniques, time, and effort. There is always a risk of oversight and misjudgment. Mirac Optical's AI image inspection system automatically identifies pests and diseases using AI and image processing technology, achieving accurate and speedy detection. 【Application Scenarios】 - Early Detection of Pests and Diseases: By continuously monitoring the growth conditions of crops, early detection of pest and disease outbreaks can prevent the expansion of damage. - Efficiency in Pesticide Application: By accurately understanding the occurrence of pests and diseases, pesticides can be applied only where necessary, reducing unnecessary chemical use. - Improvement of Quality Control: Preventing quality deterioration due to pests and diseases allows for the production of stable quality crops. - Alleviation of Labor Shortages: Automating inspection tasks that relied on experienced personnel contributes to alleviating labor shortages. 【Effects of Implementation】 - Cost reduction through reduced monitoring time and labor costs - Prevention of damage expansion through early understanding of pest and disease occurrences - Improvement of working conditions and reduction of worker burden

  • Image analysis software

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AI Inspection System "AI Hayabusa" for the Textile Industry

Reduce the burden of visual inspection! AI instantly detects foreign substances, achieving quality improvement.

In the manufacturing process of textile products, the detection of foreign substances is an important quality control item. However, visual inspections rely on human effort, leading to issues such as errors due to worker fatigue and lack of concentration, as well as variations in inspection standards. Mirac Optical's artificial intelligence inspection system, 'AI Hayabusa,' utilizes AI and image processing technology to automatically detect foreign substances, achieving stable quality control and improved work efficiency. 【Usage Scenarios】 - Detection of foreign substances in the manufacturing process of textile products - Quality control of various textile products such as woven fabrics, knitted fabrics, and non-woven fabrics - Reduction of workload from visual inspections that rely on human effort - Elimination of quality variations due to discrepancies in inspection standards 【Benefits of Implementation】 - Reduction of inspection time and improvement of work efficiency - Decrease in defective products due to improved inspection accuracy - Cost reduction by addressing personnel shortages - Stabilization of quality through the unification of inspection standards

  • Image analysis software

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Market Size Analysis Report for Pharmaceutical Imaging Systems

Global sales are expected to grow at a significant CAGR of 8.1% and reach 2.6 billion USD by 2032.

The global market size for pharmaceutical imaging inspection systems is expected to reach 1.51 billion USD by 2025. The global sales of pharmaceutical imaging inspection systems are projected to grow at a significant CAGR of 8.1%, reaching 2.6 billion USD by the end of the forecast period in 2032. Pharmaceutical imaging inspection systems have become a crucial segment of the pharmaceutical manufacturing ecosystem, which emphasizes precision, compliance, and quality assurance. The demand for such systems is increasing due to enhanced regulatory oversight, the need for serialization, and the complexity of pharmaceutical packaging formats. Imaging inspection technology integrates cameras, sensors, lighting systems, and machine learning algorithms to perform real-time analysis on production lines. With the shift towards automation and zero-defect manufacturing, vision inspection systems have become essential for global pharmaceutical companies rather than optional. [Contents] - Market overview, driving factors, challenges - Market share, forecast until 2034 (by product type, application, end-user, technology, distribution channel) - Regional market share, forecast until 2034 - Latest industry trends

  • Other analytical and testing equipment

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