Weld Defect Inspection of Battery Pack Based on Deep Learning of Linear Array Image

Tong Wu; Jincheng Yang; Ruiying Liao; Linghui Yang

doi:10.3788/LOP57.221502

[1] Guo Z Q, Lai H Q. Causes and control measures of body-in-white laser weld defects[J]. Welding Technology, 46, 43-46(2017).

[2] Liu M N[J]. Welding process analysis of NEV battery pack lower shell Automobile Technology & Material, 2018, 37-39, 42.

[3] Geng L B. Research on laser welding technology for car battery covers[D]. Harbin: Harbin Institute of Technology(2017).

[4] Cai Y X, Lu L G, Shen P et al. Online weld breakage diagnosis for the battery of electric vehicle: a data-driven approach. [C]∥2016 IEEE Vehicle Power and Propulsion Conference (VPPC), October 17-20, 2016, Hangzhou, China. New York: IEEE, 1-5(2016).

[5] Zhou L, Li X Y, Liu S X. Elimination of car body laser penetration welding defect and process optimization[J]. Foundry Technology, 36, 2336-2339(2015).

[6] Non-destructive Testing Editorial Department. nondestructive testing special report plan[J]. Nondestructive Testing, 2020, 4(2020).

[7] Wang D L. Application of ultrasonic inspection technology in detecting welding joint defects[J]. Welding Technology, 46, 82-86(2017).

[8] Chen D P, Li X L, Li Y H et al. Ultrasonic infrared thermography testing for the quality of laser welded seams[J]. Nondestructive Testing, 30, 747-749(2008).

[9] Wang Y, Guo H. Application of support vector machine in weld defect detection and recognition of X-ray images[J]. Computer Aided Drafting, Design and Manufacturing, 24, 22-26(2014).

[10] Zhu Q D, Ai X T. The defect detection algorithm for tire X-ray images based on deep learning. [C]∥2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), June 27-29, 2018, Chongqing, China. New York: IEEE, 138-142(2018).

[11] Li D J, Song H, Xu L H. A real-time welding seam detection technology based on laser vision[J]. Laser & Infrared, 49, 818-823(2019).

[12] Lu R S, Wu A, Zhang T D et al. Review on automated optical (visual) inspection and its applications in defect detection[J]. Acta Optica Sinica, 38, 0815002(2018).

[13] Wang G H, Qian K M. Review on line-scan camera calibration methods[J]. Acta Optica Sinica, 40, 0111011(2020).

[14] Lien P C, Zhao Q F. Product surface defect detection based on deep learning. [C]∥2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Scien, 250-255(2018).

[15] Everingham M. Eslami S M A, van Gool L, et al. The pascal visual object classes challenge: a retrospective[J]. International Journal of Computer Vision, 111, 98-136(2015).

[16] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[J]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788(2016).

[17] Girshick R[2020-01-30]. Fast R-CNN [2020-01-30].https:∥arxiv., org/abs/1504, 08083.

[18] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Lecture Notes in Computer Science. Cham: Springer, 9905, 21-37(2016).

[19] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[20] Guo J X, Liu L B, Xu F et al. Airport scene aircraft detection method based on YOLO v3[J]. Laser & Optoelectronics Progress, 56, 191003(2019).

[21] Chen T M, Fu G Y, Li S Y et al. Typical target detection for infrared homing guidance based on YOLO v3[J]. Laser & Optoelectronics Progress, 56, 161502(2019).

[22] Gidaris S, Komodakis N. LocNet: improving localization accuracy for object detection. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 789-798(2016).

[23] Tian Z, Gui Z G, Zhang P C et al. Faster_RCNN for industrial spark plug image weld defect inspection[J]. Journal of Test and Measurement Technology, 34, 34-40(2020).

[24] Liu H, Guo R Y. Detection and identification of SAWH pipe weld defects based on X-ray image and CNN[J]. Chinese Journal of Scientific Instrument, 39, 247-256(2018).