LAUSR

Abstract Detecting weld seams and accurately locating them from time-series images that contain strong noise pollution are difficult and lead to reduced tracking accuracy. To address this problem, this paper presents a seam detection and tracking algorithm from the perspective of seam feature extraction and seam detection and positioning. The proposed image-processing algorithm employs the powerful feature expression capability and self-learning function of the deep convolutional neural network. A real-time weld seam searching and positioning strategy based on the multi-correlation filter cooperative detection mechanism is proposed in consideration of the continuity of the motion of the feature points of adjacent frames and the correlation of laser stripe structural information. Experimental results show that the sensor's measurement frequency can reach 20 Hz, the average absolute tracking error of straight or curved welds is less than 0.25 mm, and the maximum tracking error does not exceed 1 mm in an environment with strong arc and splash noise. Moreover, the welding torch end runs smoothly during welding. The proposed strategy can meet high-quality welding requirements.