Detecting Gear Surface Defects Using Background-Weakening Method and Convolutional Neural Network

Abstract

A novel, efficient, and accurate method to detect gear defects under a complex background during industrial gear production is proposed in this study. Firstly, we first analyzed image filtering and smoothing techniques, which we used as a basis to develop a complex background-weakening algorithm for detecting the microdefects of gears. Subsequently, we discussed the types and characteristics of gear manufacturing defects. Under the complex background of image acquisition, a new model S-YOLO is proposed for online detection of gear defects, and it was validated on our experimental platform for online gear defect detection under a complex background. Results show that S-YOLO has better recognition of microdefects under a complex background than the YOLOv3 target recognition network. The proposed algorithm has good robustness as well. Code and data have been made available.