Affiliation:
1. School of Mathematics and Computer Science Gannan Normal University Ganzhou China
2. Key Laboratory of Jiangxi Province for Numerical Simulation and Emulation Techniques Ganzhou China
Abstract
AbstractSince the quality of steel is of paramount importance in modern production, the defects detection of steel surface is significantly crucial. In this field, two‐stage detection algorithms have encountered issues about low detection speed, while one‐stage detection algorithms have room for improvement in detection accuracy. How to trade‐off between accuracy and speed of detection to better meet the demands of industrial production remains a challenge. To address this problem, this paper proposes a You Only Look Once version 5 (YOLOv5)‐based improved method ACD‐YOLO. ACD‐YOLO model incorporates anchors optimization, context augmentation module, and efficient convolution operators. In anchor optimization, the boundaries of anchors are optimized using an improved genetic algorithm. Moreover, to improve detection accuracy, a context augmentation module is incorporated into both the head and the backbone end of the network. Additionally, efficient convolution operators are adopted to address the increase of computation complexity caused by the context augmentation module. Experimental results show that ACD‐YOLO achieves mean average precision of 79.3%, with frames per second of 72. Compared to reference methods, ACD‐YOLO achieves the best balance between accuracy and speed of detection, and is more suitable for practice industrial production.
Funder
Natural Science Foundation of Jiangxi Province
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software
Reference46 articles.
1. Computation of three-dimensional electromagnetic field in the eddy-current testing of steel pipes
2. Pulsed magnetic flux leakage techniques for crack detection and characterisation
3. Yin W. Chen C. Dong N. Zhang Z.:Detection of steel surface cracks based on pulsed laser point source thermal imaging method.Laster and Infrared49(10) 1195–1200(2019)
4. Development and application of the online surface inspection system for hot‐rolled strips based on led light source;Chen Y.;Baogang Technol.,2011
5. Kholief E. Darwish S. Fors M.:Detection of steel surface defect based on machine learning using deep auto‐encoder network. In:Proceedings of the 7th International Conference on Industrial Engineering and Operations Management (IEOM) 2017 pp.218–229. (2017)