基于轻量化DeepLab v3+网络的焊缝结构光图像分割  

Weld Structured Light Image Segmentation Based on Lightweight DeepLab v3+Network

作　　者：陈兵 贺晟 刘坚[1] 陈圣峰 路恩会 Chen Bing;He Sheng;Liu Jian;Chen Shengfeng;Lu Enhui(State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University,Changsha 410082,Hunan,China;School of Mechanical Engineering,Yangzhou University,Yangzhou 225009,Jiangsu,China)

机构地区：[1]湖南大学汽车车身先进设计制造国家重点实验室,湖南长沙410082 [2]扬州大学机械工程学院,江苏扬州225009

出　　处：《中国激光》2023年第8期41-50,共10页Chinese Journal of Lasers

基　　金：国家重点研发计划(2017YFE0128400);湖南省科技创新项目(S2021GCZDYF0584);湖南创新型省份建设专项资金资助项目(2020GK2013)。

摘　　要：基于激光结构光视觉传感的焊缝跟踪技术将焊缝定位转化为结构光条纹特征点的定位,具有较强的普适性。然而,实时焊缝跟踪中弧光(焊接电弧产生的强烈可见光)、飞溅(溅射焊渣)、烟尘等噪声对结构光图像造成了严重的污染,从而影响了焊缝定位的精度和鲁棒性。滤除结构光图像中的噪声对于提升焊缝定位的精度和鲁棒性具有重要作用。为滤除焊缝结构光图像中的噪声,本文提出了一种基于轻量化DeepLab v3+语义分割网络的焊缝结构光图像分割方法,通过分割出结构光条纹的前景图像来达到噪声滤除的目的。采用浅层Resnet-18网络替代DeepLab v3+的原始深层骨干网络,以提高分割效率;以像素占比的补数为权重设计了加权交叉熵损失函数,以提高结构光条纹分割的像素准确率。实验结果表明:所提方法的平均单张图像推理时间为15.9 ms,结构光条纹的像素准确率为96.47%,结构光条纹的平均交并比为89.04%,可以实现高效、精确、鲁棒的结构光图像分割,从而达到焊缝结构光图像中弧光、飞溅、烟尘等噪声滤除的目的。Objective Seam tracking technology based on laser structured light vision sensing,which transforms weld positioning into the positioning of structured light stripe feature points,has strong universality and robustness.It is regarded as the most promising seam tracking solution for engineering implementation.However,arc light,splashes,and fumes in real-time seam tracking can severely contaminate the structured light image,which affects the accuracy and robustness of weld positioning.In addition,the welding site typically provides limited computing power,and the real-time performance of weld positioning directly affects welding efficiency and quality.Accurate and efficient filtering of noise in structured light images can effectively improve the accuracy and efficiency of weld feature positioning,which is valuable in improving welding quality.This study proposes a structured light image segmentation method based on a lightweight DeepLab v3+semantic segmentation network.It implements noise filtering of arc light,spatters,and fumes,to achieve accurate and efficient noise filtering of weld structured light images by segmenting laser structured light stripes.Methods A method for weld structured light image segmentation based on a lightweight DeepLab v3+semantic segmentation network was proposed in this study to filter the noise in the weld structured light image.First,the image characteristics of the structured light of the weld in the dataset were analyzed.Most positions of structured light stripes in the structured light images can be easily distinguished from the image background,except for the region with significant aliasing between structured light stripes and noise.Using a shallow network can also result in significant expressiveness for this problem.Therefore,the Resnet-18 network was adopted to replace the original backbone network.This improved the inference speed of the DeepLab v3+semantic segmentation network.Second,the ratio of pixels occupied by structured light stripes to the image background in the weld

关 键 词：激光技术 图像处理 图像分割 DeepLab v3+网络 激光结构光 焊缝跟踪 焊缝定位 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]