基于改进CeiT的GTAW焊接熔透状态识别方法  

Identification method of GTAW welding penetration state based on improved CeiT

作　　者：王颖[1] 高胜[1] WANG Ying;GAO Sheng(Northeast Petroleum University,Daqing,163318,China)

机构地区：[1]东北石油大学,大庆163318

出　　处：《焊接学报》2024年第4期26-35,42,I0004,I0005,共13页Transactions of The China Welding Institution

基　　金：国家自然科学基金资助项目(61702093);国家重点研发计划项目(2018YFE0196000);黑龙江省自然科学基金(F2018003);黑龙江省博士后专项(LBH-Q20077);东北石油大学青年科学基金项目(2020QNL-10).

摘　　要：针对熔池信息与背景相似度高、噪声多、预测实时性差、识别精度低等问题,提出了基于改进CeiT网络模型的GTAW焊接熔透状态识别方法.首先通过MobileNetV3对Image-to-Tokens模块进行轻量化改进,提升预测的实时性能;其次设计DGCA模块改进LeFF模块来增强特征间的远程依赖关系、丰富类标记中所包含的分类信息;最后将LeFF模块中的底层特征和高层语义特征进行融合,提高模型对熔池特征的表示能力.仿真结果表明,与MobileNetV3,ResNet50,ShuffleNetV2,DeiT和CeiT模型相比,所提出的模型获得了更高的准确率和较快的检测速度.Aiming at the problems of high similarity between melt pool information and background,much noise,poor realtime prediction and low recognition accuracy,a GTAW welding fusion state recognition method based on improved CeiT network model is proposed.First,the Image-to-Tokens module is lightened and improved by MobileNetV3 to enhance the real-time performance of prediction;second,the DGCA module is designed to improve the LeFF module to enhance the remote dependencies among features and enrich the categorical information contained in the class labels;and lastly,the fusion of the underlying features and the high-level semantic features in the LeFF module improves the model's ability to represent the features of the melt pool.Simulation experiments show that the proposed model obtains higher accuracy and faster detection speed compared with MobileNetV3,ResNet50,Shuffle-NetV2,DeiT,and CeiT models.

关 键 词：图像处理 熔池 熔透状态 CeiT网络 熔化极气体保护焊 

分 类 号：TG409[金属学及工艺—焊接]