Metal Gear Surface Defect Detection Algorithm Based on Improved YOLOv8s

doi:10.3969/j.issn.1006-2475.2025.01.016

Abstract

Abstract: Aiming at the existing problems such as low identification accuracy， slow detection speed and difficult deployment in real-time detection of metal gear surface defects， a metal gear defect detection algorithm YOLO-GEAR is proposed to improve the efficiency and accuracy of detection and ensure product quality. Firstly， the lightweight module C2f-Faster is designed in the feature extraction layer， which greatly reduces the number of parameters and calculation amount of the model， so as to improve the model detection speed. Secondly， EMA attention module is added to improve the efficiency and accuracy of feature extraction. Finally， the bidirectional feature fusion structure BiFPN is introduced to enhance the feature fusion capability. The experimental results show that the average accuracy of the proposed algorithm on the test set is increased by 3.2% compared with the improvement before， the detection speed reaches 153.8 FPS， and the memory of the network model is only 6.2 MB. It is verified that the algorithm has the advantages of high recognition accuracy， fast detection speed， and small model memory ratio， which is helpful for the realization of industrial deployment.

Key words: , metal gear defect detection, lightweight, EMA attention, BiFPN

CLC Number:

TP391.41

TU Fuquan, QI Yanqi, LIU Jian, WANG Shufeng. Metal Gear Surface Defect Detection Algorithm Based on Improved YOLOv8s[J]. Computer and Modernization, 2025, 0(01): 100-106.

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