Abstract：Magnetic pulse crimping technology has high forming speed and efficiency, and is suitable for the connection of high strength steel and aluminum, carbon fiber or other lightweight materials. It has wide application prospect in aircraft industry. However, there are few online detection methods for magnetic pulse crimping pipe, which is not conducive to realize automated production of the technology. A visual detection method based on improved YOLOv4–Tiny (You only look once v4–Tiny) detection network and adaptive image processing was proposed to meet the requirement of online detection of crimping quality of magnetic pulse pressure tubing. Efficient channel attention (ECA) module was introduced to improve the YOLOv4–Tiny detection network, and an adaptive crimping depth extraction algorithm was designed based on adaptive threshold segmentation algorithm and Canny edge detection algorithm. A batch of magnetic pulse crimping pipes images were collected in a simulated industrial environment and were divided into training set and verification set. Finally, the algorithm was trained with the training data set, and the detection model obtained by training was verified by the verification set. The average precision (AP@0.5) of the crimping area detection model is 100% when the intersection ratio threshold is 0.5, and the average precision (AP@0.5:0.8) is 93.14% when the intersection ratio threshold is 0.5, 0.6, 0.7 and 0.8, and the running time per frame is 1.66ms. For image processing edge extraction algorithm, verification results show that the average deviation is 0.85 pixels, the maximum deviation is 2.6 pixels, and the running time of a single frame is 3.49ms. The average deviation of the whole crimping depth detection algorithm is 0.313 pixels, the mean square error is 0.115 square pixels, the deviation ratio is 1.35%, and the running time of a single frame is 124.49ms. In conclusion, the proposed algorithm can accurately and quickly extract the crimping depth of magnetic pulse crimping pipe without additional positioning. The algorithm has low deployment cost, high robustness and high application value.
李光耀，刘枭，赖铭，蒋浩，崔俊佳. 基于自适应视觉检测的磁脉冲压接管件接头深度智能检测算法研究[J]. 航空制造技术, 2022, 65(7): 54-63.
LI Guangyao, LIU Xiao, LAI Ming, JIANG Hao, CUI Junjia. Research on Intelligent Crimping Depth Detection Algorithm for Magnetic Pulse Crimping Pipe Based on Adaptive Vision. Aeronautical Manufacturing Technology, 2022, 65(7): 54-63.
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