基于改进U-Net算法的焊缝特征识别研究

Research on weld feature recognition based on improved U-Net algorithm

针对基于激光视觉机器人焊接过程中,由于噪声干扰导致焊缝激光条纹分割精度降低,不能精确焊接的问题,提出了一种改进U-Net算法的焊缝特征识别方法。改进型U-Net算法使用Mobile-Net作为主干网络,加强了网络的特征识别能力,并减少了模型的参数量。在编码与解码之间加入超强通道注意力机制,实现了特征的加权融合。模型采用混合损失函数,平衡了激光条纹在图像中的占比。在焊接机器人焊缝跟踪实验平台部署网络模型,实验结果表明,改进的U-Net算法平均交并比(Mean Intersection over Union,MIoU)为89.83%,像素准确率(Pixel Accuracy,PA)为99.54%,平均像素准确率(Mean Pixel Accuracy,MPA)为97.28%,处理图像的时间为0.209 s,相比其他算法,具备更优的分割精度和较快的处理速度,可以更好地应用于有干扰的机器人焊接场景中。

Aiming at the problems of laser vision robots during the welding process,the laser stripe segmentation accuracy of the weld seam was reduced due to noise interference and the inaccurate welding,an improved U-Net weld seam feature recognition method was proposed.The improved U-Net algorithm incorporates Mobile-Net as the underlying network,thereby augmenting the network′s capability for feature recognition and reducing the number of model parameters.Efficient channel attention network was added between encoding and decoding to achieve weighted fusion of features.The model adopted a mixed loss function to balance the proportion of laser stripes in the image.The network model was implemented on the experimental platform for seam tracking in welding robots.Experimental results showed that the Mean Intersection over Union(MIoU)of the improved U-Net algorithm was 89.83%,the Pixel Accuracy(PA)was 99.54%,the Mean Pixel Accuracy(MPA)was 97.28%,and the image processing time was 0.209 s.Compared with other algorithms,it has better segmentation accuracy and faster processing speed.It can be more effectively applied to robot welding scenarios with interference.