基于改进Deeplabv3+算法的滚珠丝杠驱动表面点蚀缺陷检测  

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

作　　者：郎朗[1] 陈晓琴 刘莎[2] 周强[3] LANG Lang;CHEN Xiaoqin;LIU Sha;ZHOU Qiang(School of Intelligent Manufacturing,Chongqing Three Gorges Vocational College,Chongqing 404155,China;The National Key Laboratory of Wireless Communications(NKLWC),University of Electronic Science and Technology,Chengdu 611731,China;School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[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

出　　处：《计算机科学》2024年第S01期588-593,共6页Computer Science

基　　金：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)。

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。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.

关 键 词：滚珠丝杠驱动 缺陷检测 Deeplabv3+ 多尺度特征 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]