Detection algorithm for bearing roller end surface defects based on improved YOLOv5n and image fusion

Abstract

Abstract For the current problems of low accuracy and poor reliability of defect detection for bearing roller end surfaces in industrial production, this paper proposes a bearing roller end surface defect detection algorithm based on improved YOLOv5n and the fusion of gamma-corrected maps and curvature maps. First, this paper uses photometric stereo vision to reconstruct the three-dimensional shape of the surface and proposes an improved Frankot–Chellappa integration algorithm to solve the problem of reconstructing surface deformation. Secondly, the DenseFuse network is used to fuse gamma-corrected maps and curvature maps to generate an image dataset that combines the strengths of both images to enhance defect features and improve the precision of target detection. Finally, the improved target detection model YOLOv5n is proposed to detect defects in the end surfaces of bearing rollers. The experimental results show that by using fused images for training, detection models with higher mean average precision(mAP) than traditional images can be obtained, and the improved YOLOv5n algorithm maintains the high real-time performance of the original algorithm while the mAP0.5 and mAP0.5:0.95 of improved YOLOv5n are 98.6% and 87.4%, respectively, which are respectively 0.9% and 2.8% higher than YOLOv5n.