[Free Offer] Comprehensive Coverage of Basic Knowledge with FAQ and Glossary on Appearance Inspection AI
Confide for Factory
【FAQ/Glossary】You can understand the overview of the FAQ and terminology related to the introduction of AI for difficult-to-understand visual inspections.
We will organize and provide FAQs and a glossary in an easy-to-understand manner for those considering the introduction of an appearance inspection AI system. This content is essential for companies that have already implemented appearance inspection and those that are considering doing so in the future. The appearance inspection AI system is a powerful tool designed to dramatically improve quality control in manufacturing. Our AI technology utilizes advanced image analysis and machine learning algorithms to detect defective products quickly and accurately. It achieves a highly transparent decision-making process through Explainable AI (XAI) and reliable quality assurance through Quality Assurance AI (QAAI). Furthermore, it can build high-precision models from a small amount of data using data augmentation techniques. It has the flexibility to learn from images taken with various cameras and can adapt to different imaging conditions. It maintains consistent detection accuracy even with images captured in different environments, such as those using rule-based image inspection, significantly improving factory production efficiency.
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basic information
【Specifications】 1. Operating Interface: Intuitive UI/UX 2. Data Learning: Supports learning from a small amount of data 3. Technology: Domain-specific data augmentation technology, XAI (Explainable AI), QAAI (Quality Assurance AI) 4. Annotation Tool: Automatic annotation, multi-class annotation 5. Data Management: Dataset version control features 6. Learning Algorithms - Good Product Learning (Unsupervised Learning): Learning AI models using only OK data - Defective Product Learning (Supervised Learning): High-precision anomaly detection using both OK and NG data 7. Sample Generation Function: Generation of NG data 【Operating Environment】 - Cloud: Low cost, no infrastructure management required, continuous updates - On-Premises: Customization, integration with existing systems *For more details, please refer to the 'Related Catalog' and contact us.
Price information
Negotiable *Please contact us as it varies depending on requirements and configuration. 【License Option Examples】 1. Cloud-based license 2. On-premises license 3. Cameras and lighting are required separately.
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Applications/Examples of results
【Examples of Use】 1. Semiconductor Manufacturing: Wafer inspection, chip appearance inspection, packaging defect inspection 2. Casting Industry: Surface inspection of cast products, internal defect detection, product dimension inspection 3. Automotive Manufacturing: Inspection of scratches and dents on body panels, defect detection of engine parts, quality inspection of interior parts 4. Processed Food Industry: Packaging defect inspection, foreign object detection in contents, error detection in labels 5. Chemical Industry: Appearance inspection of chemical products, defect detection in packaging, confirmation of product uniformity 6. Pharmaceutical Manufacturing: Inspection of chipping and cracking in tablets, foreign object detection in packaging, error detection in labels *We have prepared "product materials" and "case studies," so please contact us.
catalog（7）
Download All Catalogs![[White Paper] Appearance Inspection AI: FAQ and Glossary "Fundamental Knowledge and Practical Information on Appearance Inspection Using AI Technology!"](https://image.mono.ipros.com/public/catalog/image/01/4b0/769125/IPROS42390752415983853803.jpeg?w=120&h=170)
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Company information
Our company is an AI startup founded by researchers from the University of Tokyo and the French research institution Inria, with a mission to "improve quality and efficiency through advanced AI technology" specifically for the manufacturing and logistics industries. Our technology meets the needs of quality control, production technology, and DX promotion departments, enabling optimization of production processes and risk management. The founder, Yamamoto, has conducted AI research at Yahoo Japan and Inria, presenting results at top international conferences such as WWW and RecSys. Over 80% of our team consists of researchers who studied AI and computer science at world-class universities like the University of Tokyo, Cambridge, and Imperial College. With their international perspectives and advanced skills, we provide innovative solutions to quality control challenges. In particular, in mission-critical areas that emphasize the interpretability of AI, our XAI (explainable AI) technology brings transparency to the reasoning behind AI decisions for managers in quality control and production technology departments, supporting safer and more reliable system operations. This leads to improvements in production efficiency and quality, promoting sustainable growth in the manufacturing and logistics industries.