基于被动式视觉传感器的焊缝提取算法受自然光照等条件的制约，发展较为缓慢。针对这一问题，提出了一种基于本征图像分解的焊缝提取算法。首先，通过预处理抑制原始焊缝图像中的噪声；其次，基于梯度稀疏先验，将预处理图像分解为前景图层和背景图层，其中，前景图层仅包含图像边缘等结构信息，背景图层中则包含光照等平滑信息；最后，对前景图层进行Gamma变换，增强焊缝区域特征，削弱光照条件对算法的影响。试验结果表明，该算法满足检测的准确度和实时性要求，达到了辅助爬壁机器人定位焊缝位置的目的。

Due to the constraints of natural lighting and other conditions, the development of weld seam extraction algorithms based on passive vision sensors is relatively slow. Aiming at this problem, this paper proposes a welding seam extraction algorithm based on intrinsic image decomposition. Firstly, the noise in the original weld image is suppressed by preprocessing, and secondly, the preprocessed image is decomposed into foreground and background layers based on gradient sparse prior. Among them, the foreground layer only contains structural information such as image edges, while the background layer contains smooth information such as lighting. Finally, Gamma transform is performed on the foreground layer to enhance the characteristics of the weld area and weaken the influence of lighting conditions on the algorithm. The experimental results show that the algorithm meets the requirements of the accuracy and real-time performance, and achieves the purpose of assisting the wall-climbing robot to locate the weld position.