Yolov8s-DDC: A Deep Neural Network for Surface Defect Detection of Bearing Ring

Timely detection and handling of bearings with surface defects are crucial for ensuring the reliability of mechanical devices. Bearing surfaces often exhibit complex machining textures and residual oil, with defects varying in type, shape, and size. To tackle this issue, this paper proposes an improved bearing surface defect detection model, Yolov8s-DDC. First, Depthwise Separable Convolution is introduced into the backbone network, which not only reduces computational complexity and the number of parameters but also enhances the ability to capture spatial and channel information during feature extraction. Next, a Diverse Branch Block is incorporated into the neck network, utilizing diversified branch structures to capture different feature dimensions, thereby providing more comprehensive information and promoting richer feature representation. Additionally, a new module, CMA, is proposed by combining Monte Carlo Attention, which enhances the network’s feature extraction capability and improves its ability to capture information at different scales. Finally, extensive experiments were conducted using a defect dataset constructed with bearing surface defect images collected from actual industrial sites. The experimental results demonstrate that the proposed Yolov8s-DDC model achieves an average precision (mAP) of 96.9%, surpassing current mainstream defect detection algorithms by at least 1.5% in precision. Additionally, the model processes up to 106 frames per second (FPS), making it suitable for real-time defect detection in industrial settings. The experimental results validate that Yolov8s-DDC not only enhances detection accuracy but also meets the speed requirements for online bearing defect detection. The findings highlight the practical applicability and effectiveness of this model in real-world industrial applications.