基于融入高效通道注意力的DeepLabV3+焊缝识别方法  

Welding seam recognition method based on DeepLabV3+with efficient channel attention integration

作　　者：杨民强[1] YANG Minqiang(China Railway 14th Bureau Group Co.,Ltd.,Jinan 250014,China)

机构地区：[1]中铁十四局集团有限公司,济南250014

出　　处：《焊接》2025年第2期66-75,共10页Welding & Joining

基　　金：中铁十四局集团有限公司科研课题(十四局科创2022-669号)。

摘　　要：【目的】焊缝识别在材料加工和焊接工艺中具有重要的应用价值。针对复杂环境下电弧光、烟雾等噪声对激光焊焊缝条纹分割精度造成的影响,提出了一种改进的DeepLabV3+焊缝识别方法,该方法融入了高效通道注意力机制(Efficient channel attention module,ECA)以增强模型的鲁棒性。【方法】在模型的解码器部分特征融合之前,引入ECA注意力机制实现特征的加权融合,再结合交叉熵损失、骰子损失和焦点损失,以进一步提升模型的准确性和鲁棒性。【结果】试验结果表明,提出的算法在实际焊接环境中的焊缝图像分割精度表现优异,平均像素准确度(Mean pixel accuracy,mPA)达到95%,平均交并比(Mean intersection over union,m IoU)为89%,能够有效提取和识别焊缝特征。【结论】通过对复杂环境下激光焊焊缝识别的试验验证,改进后的模型显著提高了焊接图像的识别性能,具有较强的应用前景。[Objective]Welding seam recognition plays a crucial role in material processing and welding process.To address the influence of welding seam stripes segmentation accuracy of laser welding due to noise such as arc light and smoke in complex environments,an improved DeepLabV3+welding seam recognition method is proposed,which incorporates the ECA module to enhance robustness of the model.[Methods]The ECA attention mechanism is introduced before the feature fusion in decoder of the model to achieve weighted feature fusion.Subsequently,a combination of cross-entropy loss,dice loss and focal loss is used to further improve accuracy and robustness of the model.[Results]Experimental results show that the proposed algorithm achieves excellent segmentation performance in actual welding environments,with an mPA value of 95%and an mIoU value of 89%.It effectively extracts and recognizes welding seam features.[Conclusion]Experimental validation for weld seams of laser welding recognition in complex environments demonstrates that the improved model significantly enhances the performance of welding image recognition,showing strong potential for practical application.

关 键 词：焊缝识别 语义分割 高效通道注意力 DeepLabV3+ 

分 类 号：TG409[金属学及工艺—焊接]