Title: An accurate detection method for randomly distributed welding slags using an improved Yolo v3 network

Authors: Qiuping Tu; Hongdi Liu; Chao Qu; Linli Tian; Dahu Zhu

Addresses: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China ' Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China ' Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China ' Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China ' Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China

Abstract: Irregular welding slag spatters during welding of the car body are considered as extremely unavoidable defects. Detecting these tiny defects exhibiting properties in position and number is a challenging task. In this paper, a novel computational framework by modifying the structure of the Yolo v3 network is developed to accurately detect the randomly distributed welding slags. It has been verified that the improved Yolo v3 algorithm has improved accuracy by 7.4%, recall rate Rr by 6.8%, and real-time detection frame rate of 31.25 fps compared with Yolo v3, the detection performance is excellent in comparison with Faster RCNN and Yolo v3. Therefore, the improved algorithm can improve the accuracy of welding slag detection, which is beneficial to real-time welding slag detection and promote automated production and processing in industry.

Keywords: defect detection; welding slags; Yolo v3; Darknet53; recall rate.

DOI: 10.1504/IJCMSSE.2021.10045543

International Journal of Computational Materials Science and Surface Engineering, 2021 Vol.10 No.3/4, pp.195 - 208

Received: 30 Nov 2020
Accepted: 12 May 2021
Published online: 07 Mar 2022 *

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