为了在复杂噪声环境下从焊缝图像中精确地提取结构光条纹,构建了语义分割与目标检测相结合的深度学习模型用于焊缝图像的检测。为了提高模型的检测速度,在语义分割分支中,通过添加并行下采样模块及缩减卷积核数量的策略对模型进行了优化,并使该分支与目标检测分支的特征提取部分共享权重。针对焊缝图像中结构光条纹与背景像素比例失衡而导致模型分割结果偏向负样本的问题,在损失函数中添加Dice系数来对模型进行修正。经实验验证,该方法在保证实时性的基础上,以较高的精度实现了结构光条纹的检测。

In order to accurately extract structured light stripes from weld images in the complex noise environment, we proposed a deep learning model combining semantic segmentation with object detection to detect the weld images. In the semantic segmentation branch, the model was optimized by adding parallel downsampling modules and reducing the number of convolution kernels to increase the detection speed, and the feature extraction parts of this branch and the object detection branch shared the weights. Aiming at the problem that the proportion unbalance of structured light stripes and background pixels in the weld images caused the model segmentation results to be biased towards negative samples, we introduced a Dice coefficient into the loss function to correct the model. The experimental results show that the proposed method can achieve the extraction of structured light stripes with high accuracy on the basis of ensuring real-time performance.