Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 240200058-6.doi: 10.11896/jsjkx.240200058

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Detection of Pitting Defects on the Surface of Ball Screw Drive Based on Improved Deeplabv3+ Algorithm

LANG Lang1, CHEN Xiaoqin1, LIU Sha2, ZHOU Qiang3   

  1. 1 School of Intelligent Manufacturing,Chongqing Three Gorges Vocational College,Chongqing 404155,China
    2 The National Key Laboratory of Wireless Communications(NKLWC),University of Electronic Science and Technology,Chengdu 611731,China
    3 School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Published:2024-06-06
  • About author:LANG Lang,born in 1983,postgra-duate,associate professor.His main research interests include machine lear-ning,image processing,and anomaly detection.
  • Supported by:
    Science and Technology Research Project of Chongqing Education Commission(KJQN202103509) and Chongqing Teaching Reform Research Project(GZ223108,GZ223113).

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Abstract: Aiming at the problems of complex background environments,small pitting defect targets,and difficulty in detection on the surface of ball screw drives,an improved Deeplabv3+ algorithm for segmenting surface defects of ball screw drives is proposed.This algorithm adopts Re2Net-50 to replace the backbone network of Deeplabv3+,significantly enhances the ability to recognize small-sized defect targets.Additionally,by integrating feature pyramid networks(FPN) into the backbone network,the algorithm effectively extracts multi-scale information,thereby improving the precise localization of defect targets.Finally,the coordinate attention mechanism is introduced after the ASPP module of the Deeplabv3+ network,enhancing the model’s focus on spatial dimensions within the image and effectively capturing long-range spatial dependencies.Experimental results demonstrate that,compared to the original Deeplabv3+,the proposed algorithm shows a 4.38% improvement in the mean intersection over union(MIoU) metric,a 5.52% increase in accuracy,and a 2.74% rise in F1-score.Furthermore,when compared with other classic semantic segmentation algorithms,the proposedalgorithm also exhibits certain superiority.

Key words: Ball screw drive, Defect detection, Deeplabv3+, Multi-scale features, Attention mechanism

CLC Number: 

  • TP391
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