Dynamic Vision Sensor Based Defect Detection for the Surface of Aluminum Disk

Current visual defect detection technologies usually rely on conventional charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) cameras for defect imaging and the development of backend detection algorithms. However, these technologies encounter challenges such as slow imaging speed, limited dynamic range, and significant background interference, which hinder the rapid detection of minor defects on highly reflective product surfaces. To address these challenges, we innovatively propose a new defect detection mode based on dynamic vision sensor (DVS) to achieve efficient defect detection on the highly reflective surfaces of aluminum disks. DVS is a novel bio-inspired visual sensor with advantages such as fast imaging speed, high dynamic range, and excellent ability to capture moving objects. First, we conduct DVS imaging experiments for minor defects on the highly reflective surfaces of aluminum disk and analyze the characteristics and advantages of DVS on defect imaging. Then, we establish the first event-based defect detection dataset (EDD-10k) based on DVS, including three common defect types: Scratch, point and stain. Finally, to address the issues such as varying defect shapes, sparse textures, and noise interference, we propose a temporal irregular feature aggregation framework for event-based defect detection (TIFF-EDD), and realize the effective detection of defect targets. © 2024 Science Press. All rights reserved.