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

Abstract EN

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.