An Engine Cylinder Surface Defect Detection Algorithm Based on the YOLOv5 Network and Pix2Pix Model

doi:10.3901/JME.2025.02.046

Abstract

Abstract: In the industrial vision application for engine cylinder surface defect detection, the lack of defect datasets leads to low detection accuracy and efficiency. An engine cylinder surface defect detection algorithm based on the YOLOv5 network and Pix2Pix model is proposed. The generative adversarial network pix2pix model is used for dataset enhancement. The image pre-processing methods, such as the grayscale enhancement method and band-pass filtering method, are used to highlight the surface defect features. The defect detection algorithm is developed based on the YOLOv5 model. Then a cylinder surface defect detection hardware and software system is established using the above framework. Experimental validation is conducted on the production line in the industry. Results show that the detection accuracy of the proposed method is 98.4%. Single image detection takes less than 0.5 s. This research has the capacity to deal with the lack of datasets in deep learning training, which improves the detection accuracy and efficiency of engine cylinder surface defect detection. The proposed method shows great potential for industrial inspection applications.

Key words: engine cylinder, defect detection, YOLOv5, pix2pix

CLC Number:

TP391

ZENG Zhilin, QU Hao, DU Zhengchun. An Engine Cylinder Surface Defect Detection Algorithm Based on the YOLOv5 Network and Pix2Pix Model[J]. Journal of Mechanical Engineering, 2025, 61(2): 46-55.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2025.02.046

http://www.cjmenet.com.cn/EN/Y2025/V61/I2/46

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