SIA-YOLO: A Lightweight Multi-scale Feature Fusion Network For Bearing Surface Defect Detection

Bearing surface defect detection is a key task in manufacturing quality control. However, traditional detection methods often fail to meet the requirements in terms of accuracy and efficiency when faced with defects of small size, diverse shapes and complex backgrounds. To solve this problem, this paper proposes a lightweight multi-scale feature fusion network based on YOLOv11. Firstly, the lightweight New StarNet module is used as the backbone to extract features by stacking multiple star operation blocks, while downsampling is performed using convolutional layers, and nonlinear mapping is achieved through element-wise multiplication. This improves the model's feature extraction capability while reducing inference overhead through lightweight calculation. Secondly, the IRMA attention module is embedded in the neck, so that the model can better extract important features of the bearing surface, while enhancing the small target detection capability and keeping the model lightweight. Finally, the improved AFPN module is used to optimize the detection head, which significantly enhances the model's feature expression capability and effectively improves the model's detection capability for multi-scale defects. Experiments show that the GFLOPs of the SIA-YOLO algorithm on ZC bearing dataset is reduced from 6.4GFLOPs of YOLOv11 to 4.2GFLOPs, a reduction of 34.4 . The mAP@0.5 of the SIA-YOLO algorithm increased by 1.6 from 87.5 to 89.1 . A large number of ablation and comparative experiments have verified the effectiveness and generalization ability of the model in bearing surface defect detection.