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

摘要

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.