{"type":"ScholarlyArticle","title":"FD-UAD: Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement","authors":[{"name":"Yang Chang","slug":"yang-chang","given":"Yang","family":"Chang","affiliation":"School of Information Science and Technology , Fudan University , Shanghai , China"},{"name":"Yuxuan Lin","slug":"yuxuan-lin","given":"Yuxuan","family":"Lin","affiliation":"School of Computer Science , Fudan University , Shanghai , China"},{"name":"Boyang Wang","slug":"boyang-wang","given":"Boyang","family":"Wang","affiliation":"Academy for Engineering and Technology , Fudan University , Shanghai , China"},{"name":"Qing Zhao","slug":"qing-zhao","given":"Qing","family":"Zhao","affiliation":"Academy for Engineering and Technology , Fudan University , Shanghai , China"},{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":"Academy for Engineering and Technology , Fudan University , Shanghai , China"},{"name":"Wenqiang Zhang","slug":"wenqiang-zhang","given":"Wenqiang","family":"Zhang","affiliation":"School of Computer Science , Fudan University , Shanghai , China; Academy for Engineering and Technology , Fudan University , Shanghai , China"}],"abstract":"In industrial quality control, detecting defects is essential. However, manual checks and machine vision encounter challenges in complex conditions, as defects vary among products made of different materials and shapes. We create FD-UAD, Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement. It uses multi-sensor technology, combining RGB and infrared imaging, liquid lenses for adjustable focal lengths, and uses image fusion to capture multidimensional features. The system incorporates image restoration techniques such as enhancement, deblurring, denoising, and super-resolution, alongside unsupervised anomaly detection model for enhanced accuracy. FD-UAD is successfully used in a top diesel engine manufacturer, demonstrating its value in AI-enhanced industrial applications.","doi":"10.24963/ijcai.2024/993","url":"https://ijcai.org/proceedings/2024/993","pdf":"https://ijcai.org/proceedings/2024/0993.pdf","markdown":"https://ijcai.org/proceedings/2024/993.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/993","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":993,"pages":{"first":8619,"last":8622},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Angiulli and Pizzuti, 2002] Fabrizio Angiulli and Clara Piz- zuti. 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