Opto-Electronic Engineering, Volume. 52, Issue 3, 240275(2025)
Construction of convolutional neural network model for micro-scale bump on metal pipe fittings
The low detection rate of tiny defects on the surface of metal pipe fittings is a key issue confronting industrial component inspection. In aiming at this problem, an improved YOLOv9-MM model was constructed to improve the accuracy of small target detection. A real-time image acquisition system for precision metal pipe fittings was designed. By using an annular light source combined with a telecentric lens, the surface of pipe fittings can be snapped by the CCD camera and covered at all angles to eliminate the problem of missing areas. The feature map extracted methods of shallow network were introduced, and the upper sampling module of Dysample was combined to realize the dynamic fusion of depth features. By improving the loss function, the precision of small target detection is greatly improved. The results show that the proposed method has an average detection accuracy of 70.2% and a detection speed of 90 f/s. The proposed method shows some feasibility in the actual application.
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Zihao Liu, Guohao Tao, Feng Xue, Yebo Lu, Jun Yang. Construction of convolutional neural network model for micro-scale bump on metal pipe fittings[J]. Opto-Electronic Engineering, 2025, 52(3): 240275
Category: Article
Received: Nov. 26, 2024
Accepted: Feb. 17, 2025
Published Online: May. 22, 2025
The Author Email: Jun Yang (杨俊)