A lightweight and efficient bearing defect detection algorithm DWA-YOLO is proposed to address the challenges of large scale variation, similar texture, and dense distribution of defects in the surface defect detection of bearing outer rings, as well as the complexity of existing detection model structures, poor computational complexity, and detection accuracy. Firstly, a plug and play lightweight dual bottleneck structure module DBM was designed to effectively reduce model complexity and enhance the model′s ability to extract features at different scales. Secondly, the wavelet convolution WTConv with multi-scale characteristics is introduced as a downsampling operator in the network backbone. By expanding the receptive field of the model and utilizing the multi-scale analysis characteristics to capture the details and texture information of the image, the model′s anti-interference ability against texture and noise and its ability to understand contextual information are enhanced, thereby improving the overall detection accuracy. In addition, this article designs a joint loss function Alpha-MPDIOU, which utilizes power transformation mechanism to improve the localization accuracy of bounding boxes and solve the problem of detecting multiple boxes. Finally, the use of auxiliary detection head training strategy accelerates the convergence speed of the model and enhances its detection capability. The experimental results show that DWA-YOLO improves mAP accuracy by 3.5% compared to the baseline model, with a model parameter size of 2.6 M and a computational complexity of 7.4 GFLOPs. The improved model not only enhances the ability to identify bearing defects, but also reduces network complexity, making it more suitable for the detection needs of bearing outer ring surface defects in industrial sites.