Improving the Algorithm Study of YOLO in Steel Surface Defect Detection

Abstract

To solve the problem of low detection accuracy caused by background interference and diverse target forms, a series of improvements are proposed to improve the detection accuracy. According to the various characteristics of steel surface defects, this paper presents the K-Means clustering algorithm to optimize the clustering results and quickly and accurately obtain the size of the prior box. In view of the small proportion of the target defect area in the overall image and background interference, a two-way attention module (TWA-Block) is proposed to establish the long-distance dependence of the spatial domain and channel domain features, and a background suppression function is designed to realize the division of defect areas. Experiments of the proposed improvements in the NEU-DET dataset based on the YOLO series model show that the detection accuracy of all the improved YOLO series models has improved, and the number of parameters will not increase substantially