基于CNN-Transformer混合网络的焊缝激光条纹分割  

Laser Stripe Segmentation of Weld Seam Based on CNN⁃Transformer Hybrid Networks

作　　者：王颖[1] 高胜[2] 戴哲 Wang Ying;Gao Sheng;Dai Zhe(School of Computer&Information Technology,Northeast Petroleum University,Daqing 163318,Heilongjiang,China;School of Mechanical Science and Engineering,Northeast Petroleum University,Daqing 163318,Heilongjiang,China)

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318 [2]东北石油大学机械科学与工程学院,黑龙江大庆163318

出　　处：《中国激光》2024年第24期105-116,共12页Chinese Journal of Lasers

基　　金：国家自然科学基金(61702093);国家重点研发计划(2018YFE0196000);黑龙江省自然科学基金(F2018003);黑龙江省博士后专项(LBH-Q20077);黑龙江省优秀青年基础研究支持计划(YQJH2023073)。

摘　　要：针对复杂焊接环境下大量弧光噪声造成焊缝激光条纹分割不完整、精度低、实时性不足的问题,采用编码器-解码器的结构,建立一种CNN-Transformer混合的焊缝激光条纹分割网络模型。模型的编码器部分使用参数量和计算量较小的MobileViT模块进行特征提取,并嵌入双路非局部模块捕获焊缝图像空间域和通道域上的长距离关联关系,在保证特征提取能力的同时提高分割效率。模型的解码器部分使用亚像素卷积神经网络,获得语义分割结果,减少信息重构过程中的特征损耗,提高模型对焊缝激光线条的提取性能。为解决焊缝图像中激光条纹像素与背景像素占比不均衡问题,提出一种动态干预激光条纹加权系数的损失函数。为验证所提模型的有效性,利用现场复杂焊接环境下获取的数据进行模型训练,像素精度达到98.0%,平均像素精度达到96.6%,平均交并比达到92.1%,单张图片推理时间仅为40 ms。与Unet、Deeplabv3+、SegNet、PSPNet、RefineNet和FCN-32s等常用的轻量级语义分割网络进行对比,所提模型在精度和速度方面均具有一定的优势。Objective The challenging conditions at welding construction sites—such as uneven weldment surfaces,complex bevel shapes due to the front weld channel,loss of centerline information,smoke,spatter,intense arc light,and overlapping reflections—hinder realtime and accurate tracking and control during the welding process.Projecting a laser onto the weldment surface,using a vision sensor to capture the laser streak image at the bevel,and then using the identified key point of the laser streak as the basis for weld positioning has become the most widely applied method for tracking complex weld seams.Therefore,accurately segmenting multilayer multi-pass weld laser stripes against a complex background is a key problem in intelligent welding processes.This study proposes a lightweight weld laser stripe segmentation method based on a convolutional neural network(CNN)-Transformer hybrid network to improve the segmentation accuracy and real-time performance by acquiring fine-grained features and recognizing subtle differences,thereby enabling the tracking of complex multi-layer multi-pass welds in high-noise environments.Methods This study develops a hybrid CNN-Transformer model for weld laser streak segmentation.The encoder part of the model uses the MobileViT module,which has a smaller number of parameters and demands less computation,for feature extraction.It also embeds a dual non-local block(DNB)module to capture the long-distance correlation relationship on the spatial and channel domains of the weld image,which ensures feature extraction capability and improves the segmentation efficiency simultaneously.The decoder part of the model uses efficient sub-pixel convolutional neural network(ESPCN)to obtain semantic segmentation results,which reduces the feature loss in the information reconstruction process and improves the model performance in extracting laser lines from weld seams.To address the imbalance between laser-streak and background pixels in the weld image,a loss function that dynamically adjusts the weighti

关 键 词：激光条纹分割 语义分割 TRANSFORMER MobileViT模块 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]