为了减少激光焊缝语义分割中焊缝形状和颜色多样性对分割精度的影响, 采用注意力机制的图像语义分割方法提取焊缝区域。通过把焊缝区域图像从RGB转变到HSV颜色空间, 在HSV模型空间实现对焊缝表面颜色识别, 分析了３种类型焊缝对区域分割和颜色识别的影响。结果表明, 焊缝分割区域平均像素精度约为91.2%, 添加注意力机制U型网络模型的分割效果更好。此焊缝表面颜色自动识别结果符合生产要求, 在工业生产中有广泛应用前景。

In order to reduce the influence of weld shape and color diversity on segmentation accuracy in laser weld semantic segmentation, an image semantic segmentation method based on attention mechanism was used to extract weld. The image in the weld was converted from RGB(red, green, blue) to HSV(hue, saturation, value) color space, and the weld surface color was recognized in HSV. The effects of three kinds of welds on region segmentation and color recognition were analyzed. The results show that the average pixel accuracy of the weld segmentation region is about 91.2%, and the segmentation effect of the attention U-Net model with attention mechanism is better. The results of automatic identification of weld surface color meet production requirements, and have broad application prospects in industrial production.