Active vision sensing is widely used in intelligent robotic welding for bead detection and tracking. Disturbed by welding noise such as arc light and spatter, it is a hard work to extract the laser stripe and feature values. This paper presents a method for denoising and feature extraction of weld seam profiles with strong welding noise in gas metal arc welding (GMAW) process by using stacked denoising autoencoder (SDAE). This algorithm encodes the images of various butt joints with strong welding noise to several useful intermediate representations, which can be decoded to the image of pure laser stripe in 1-pixel width. The results show little deviations when there are large spatters across the laser stripe. A back propagation neural network (BPNN) is developed to verify the reliability of the intermediate representations gotten from the encoder, in which the intermediate representations are input neurons and the weld seam width is output neuron. The average width error in training dataset and testing dataset is 0.042 mm and 0.061 mm. The results show that this algorithm can extract the weld seam profiles with strong welding noise and extract feature values accurately.

主动视觉传感广泛应用于智能机器人焊接中，用于焊道检测和跟踪。受电弧光和飞溅等焊接噪声的干扰，提取激光条纹和特征值是一项艰巨的工作。本文提出了一种使用堆叠降噪自编码器 (SDAE) 对气体保护金属电弧焊 (GMAW) 过程中具有强焊接噪声的焊缝轮廓进行降噪和特征提取的方法。该算法将具有强焊接噪声的各种对接接头的图像编码为几种有用的中间表示，可以将其解码为1像素宽度的纯激光条纹图像。结果表明，当激光条纹上有大量飞溅时，偏差很小。开发了一个反向传播神经网络（BPNN）来验证从编码器获得的中间表示的可靠性，其中中间表示是输入神经元，焊缝宽度是输出神经元。训练数据集和测试数据集的平均宽度误差分别为 0.042 毫米和 0.061 毫米。结果表明，该算法能够提取焊接噪声较强的焊缝轮廓，准确提取特征值。