Title: Weld defect detection of power battery pack based on image segmentation

Authors: Bo Tao; Fuqiang He; Quan Tang; Zhinan Guo; Hansen Long; Shidong Li; Yongcheng Cao; Guijian Ruan

Addresses: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China ' Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China ' OPT Machine Vision Tech Co., Ltd, Dongguan 523852, China ' Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China ' Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China ' Jingmen Wusan Mechanism Equipment Manufacture Co., Ltd, Jingmen 431821, China ' Jingmen Wusan Mechanism Equipment Manufacture Co., Ltd, Jingmen 431821, China ' Jiangsu Ruihong Photoelectric Technology Co., Ltd., Suqian 321300, China

Abstract: The safety and production efficiency are an important part of the power batteries production process and need to be considered seriously. Aiming at the welding quality of a power battery, a three-dimensional detection method based on the line laser sensor was proposed. Firstly, the depth data of the weld surface of the battery top cover is obtained by using a line laser sensor, and the defect area is segmented by using a multi thresholds segmentation method based on contour lines. Through the connected domain algorithm, the centres of defective areas are located. And the defect type is determined according to distance between the centres of the defect areas. Experimental results show that the detection rate reaches 97%, which indicates that the scheme has high detection accuracy and strong stability, and verifies the effectiveness of the method.

Keywords: power battery; line laser sensor; threshold segmentation; connected domain; defect classification.

DOI: 10.1504/IJWMC.2022.126363

International Journal of Wireless and Mobile Computing, 2022 Vol.23 No.2, pp.139 - 145

Received: 21 Dec 2021
Accepted: 26 Feb 2022
Published online: 24 Oct 2022 *

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