FD-UAD: Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement

Yang Chang; Yuxuan Lin; Boyang Wang; Qing Zhao; Yan Wang; Wenqiang Zhang

IJCAI 2024poster0 citations

FD-UAD: Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement

Yang Chang, Yuxuan Lin, Boyang Wang, Qing Zhao, Yan Wang, Wenqiang Zhang

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.

Computer Vision: CV: Recognition (object detection, categorization)Computer Vision: CV: Applications

BibTeX

@inproceedings{ijcai2024p993,
  title     = {FD-UAD: Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement},
  author    = {Chang, Yang and Lin, Yuxuan and Wang, Boyang and Zhao, Qing and Wang, Yan and Zhang, Wenqiang},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {8619--8622},
  year      = {2024},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2024/993},
  url       = {https://doi.org/10.24963/ijcai.2024/993},
}