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In response to the issues prevalent in industrial production's weld seam inspection systems, such as susceptibility to lighting conditions, reliance on manual operation, and high risk of collision accidents, an automated weld seam detection system based on a multi-feature fusion segmentation algorithm has been developed. The system employs a laser depth camera to capture depth images of the weld seams. It combines multi-feature fusion with skew moment envelope fitting in the image segmentation algorithm to overcome the challenges posed by uneven surfaces. A calibration algorithm ensures the accurate acquisition of weld seam coordinates and width. Additionally, a collision avoidance detection algorithm based on Oriented Bounding Boxes (OBB) is proposed to prevent collisions during the welding process. The system was tested using a robotic arm setup. Experimental results demonstrate the system's effectiveness in detecting weld seam width and positioning, with an average error of 0.3029 mm and 0.3393 mm, respectively, meeting industrial application standards. This system optimizes the welding path and collision detection, significantly enhancing welding quality and production efficiency.