基于改进YOLOv5的焊缝识别算法研究
Research on weld identification algorithm based on improved YOLOv5
周翌晨 1虞旦 1李佳成 2蔡春波 1张华军2
作者信息
- 1. 上海海事大学物流工程学院,上海 201306
- 2. 上海交通大学材料科学与工程学院,上海 200240
- 折叠
摘要
针对机器人自适应打磨焊缝的问题,文章提出一种基于YOLOv5 改进的焊缝检测算法,实现焊缝的识别和初定位.使用焊接机器人制作各类不同参数和形貌的焊缝,自制一个包含 3 996 张焊缝图像的数据集用来深度学习.选用YOLOv5s模型进行训练,在Backbone中添加了GAM注意力机制模块;同时引入GhostNet,用GhostConv模块和C3Ghost模块替换原模型的Conv模块和C3 模块.改进后的YOLOv5s-GhostNet-GAM模型的mAP@0.5 达到了90.21%,相比原YOLOv5s模型提高了 4.05%,同时参数量减少了 5.64%,FLOPs降低了 27.44%,检测速率为 23.47 FPS,达到了机器人自适应打磨焊缝对识别精度与后期软件部署的要求.
Abstract
To solve the problem of robot adaptive weld grinding,this paper proposes a weld detection algorithm based on improved YOLOv5,which realizes the identification and initial positioning of the welds.The welding robot is used to produce weld seams with different parameters and shapes,and a dataset containing 3 996 weld images is self-made for deep learning.The YOLOv5s model is selected for training,and the GAM attention mechanism module is added to the backbone.At the same time,GhostNet is introduced,and the Conv module and C3 module of the original model are respectively replaced by the GhostConv module and C3 Ghost module.The mAP@0.5 of the improved YOLOv5s-GhostNet-GAM model reaches 90.21%,4.05%higher than that of the original YOLOv5s model.At the same time,the number of parameters is reduced by 5.64%,and the FLOPs are reduced by 27.44%.The detection rate is 23.47 FPS.It meets the requirements of robot adaptive weld grinding for identification accuracy and later software deployment.
关键词
机器人焊缝打磨/焊缝识别/深度学习/YOLOv5模型/GAM注意力机制Key words
robot weld grinding/weld identification/deep learning/YOLOv5 model/GAM attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据