基于多任务学习的焊缝图像激光条纹分割与特征点定位方法研究  

Laser Fringe Segmentation and Feature Points Location Method of Weld Image Based on Multi-Task Learning

作　　者：黄义庚 王大庆 江曼 殷浩宇 高理富[1,2] Huang Yigeng;Wang Daqing;Jiang Man;Yin Haoyu;Gao Lifu(Institute of Intelligent Machines,Hefei Institute of Physical Science,Chinese Academy of Science,Hefei 230031,Anhui,China;University of Science and Technology of China,Hefei 230026,Anhui,China)

机构地区：[1]中国科学院合肥物质科学研究院智能机械研究所,安徽合肥230031 [2]中国科学技术大学,安徽合肥230026

出　　处：《中国激光》2023年第16期71-81,共11页Chinese Journal of Lasers

基　　金：国家自然科学基金重大研究计划重点支持项目(92067205);中国科学院战略性先导科技专项(A类)(XDA22040303);安徽省重点研发计划(2022a05020035);安徽省科技重大专项(202103a05020022);中国科学院合肥物质科学研究院院长基金青年项目(YZJJ2021QN25)。

摘　　要：焊缝信息的快速准确获取是实现自动化焊接的首要问题。然而,在实际焊接过程中,电弧、飞溅、强反射光等噪声会严重污染采集的图像,导致焊缝定位偏移,最终导致跟踪失败。为了提高跟踪过程中的焊缝定位精度与图像处理速度,本文提出了一种将激光条纹分割与焊缝特征点定位相结合的轻量级多任务深度学习模型。该模型由编码器和解码器组成,激光条纹分割子任务与焊缝特征点定位子任务共用编码器主干网络,解码器包含激光条纹分割分支和基于可微空间到数值转换(DSNT)的焊缝特征点定位分支,整个模型遵从轻量化设计思想,同时利用多个子任务之间的相关信息,进一步提升各子任务的性能。实验结果表明,所提模型能够有效克服各类焊接噪声,完成焊缝特征的提取,单幅图像的处理时间约为11.45 ms,特征点定位精度可达0.1872 pixel,在自动化焊接方面具有广阔的应用前景。Objective With advancements in science and technology,welding technology has progressed from manual to automated and intelligent welding.The widely used weld tracking technology based on laser vision can improve the ability of welding robots to perceive their environments,with the added advantages of non-contact and high precision.However,in real-time weld tracking,the collected weld images are often severely affected by strongly reflected light,splash,and arc noise.Therefore,laser stripes are accurately and quickly extracted from images containing a large amount of noise,and then obtaining weld information from them is a prerequisite for high-quality welding.To improve the weld location accuracy and the image processing speed in the weld tracking process,this paper proposes a lightweight multi-task deep learning model that combines laser strip segmentation and weld feature point location.The model consists of an encoder and a decoder.The laser fringe segmentation subtask and the weld feature point location subtask share the encoder backbone network.The decoder includes a laser fringe segmentation branch and a weld feature point location branch based on differentiable space-to-numerical transformation(DSNT).The entire model is designed in a lightweight manner,and it simultaneously adopts relevant information between multiple subtasks to further improve the performance of each subtask.In summary,we expect that the designed deep learning model can achieve accurate and rapid acquisition of weld features during the welding process.Methods In order to improve the weld location accuracy and image processing speed in the weld tracking process,a lightweight multi-task deep learning model combining laser strip segmentation and weld feature point location is proposed.The proposed model adopts the parameter hard sharing mechanism in multi-task learning such that the model uses fewer parameters.Specifically,the model consists of an encoder and a decoder.The encoder completes the feature extraction of weld position and edge i

关 键 词：激光技术 焊缝跟踪 轻量级 多任务 激光条纹分割 特征点定位 

分 类 号：O436[机械工程—光学工程]