计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 240200058-6.doi: 10.11896/jsjkx.240200058

• 图像处理&多媒体技术 • 上一篇    下一篇

基于改进Deeplabv3+算法的滚珠丝杠驱动表面点蚀缺陷检测

郎朗1, 陈晓琴1, 刘莎2, 周强3   

  1. 1 重庆三峡职业学院智能制造学院 重庆 404155
    2 电子科技大学无线通信国家重点实验室 成都 611731
    3 重庆邮电大学计算机科学与技术学院 重庆 400065
  • 发布日期:2024-06-06
  • 通讯作者: 郎朗(2008190232@cqsxzy.edu.cn)
  • 基金资助:
    重庆市教育委员会科技研究项目(KJQN202103509);重庆市教学改革研究项目(GZ223108,GZ223113)

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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摘要： 针对滚珠丝杠驱动表面背景环境复杂、点蚀缺陷目标小因而难以检测的问题,提出改进的Deeplabv3+滚珠丝杠驱动表面缺陷分割算法。本算法采用Re2Net-50替换Deeplabv3+的主干网络,显著提升了对小尺寸缺陷目标的识别能力。此外,通过在主干网络中融合特征金字塔网络FPN,能够加强多尺度信息的提取,从而增强了对缺陷目标的精确定位。最后,本研究在Deeplabv3+网络的ASPP模块之后引入了Coordinate Attention机制,能够增强模型对图像中空间和维度的关注,有效地捕获了图像中的长距离空间依赖关系。实验结果表明,与原始的Deeplabv3+相比,所提算法在平均交并比MIoU指标上提高了4.38%,准确率Accuracy提高了5.52%,F1-score提高了2.74%。同时,与其他经典的语义分割算法相比,所提算法也展现出了一定的优越性。

关键词: 滚珠丝杠驱动, 缺陷检测, Deeplabv3+, 多尺度特征, 注意力机制

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

中图分类号: 

  • TP391
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