Aiming at the existing bearing defect detection algorithms with low accuracy, misdetection as well as serious leakage, a bearing defect detection algorithm based on YOLOv8n (LASW-YOLOv8) is proposed to solve these problems. Based on YOLOv8n, the algorithm introduces a lightweight and efficient LiteShiftHead detection head, which is combined with SPConv, REG and CLS modules to improve the efficiency and accuracy of feature extraction, target frame regression and category classification. In addition, the algorithm also introduces the Adaptive Rotation Convolutional Kernel module (ARConv), which enhances the detection of multi-directional defects; the Neck Network Optimisation module (SAF), which further improves the efficiency of feature extraction; and the Inner-WIoU loss function, which is used to optimise the bounding box localisation accuracy and to enhance the detection of small targets and complex shape defects. Experimental results show that the LASW-YOLOv8 algorithm outperforms other mainstream algorithms in several performance indicators. The algorithm achieves an accuracy of 97.2% and a recall of 96.6%, representing improvements of 3.4% and 4.5%, respectively, compared to the original YOLOv8n. Meanwhile, mAP0.5 and mAP0.5:0.95 achieved 98.9% and 73.3%, respectively, and ran at 83 fps. These results fully demonstrate the effectiveness of the proposed algorithm, which not only effectively reduces the phenomenon of false detection and missed detection, but also meets the requirements of high accuracy and real-time performance in industrial inspection. In addition, in the experiments on the Northeastern University public dataset (NEU-DET), the LASW-YOLOv8 algorithm outperforms other mainstream algorithms in the four key metrics of accuracy, recall, mAP0.5, and mAP0.5:0.95, which are 79.3%, 79.9%, 84.1%, and 49.1%, respectively. This performance proves that the LASW-YOLOv8 algorithm has excellent generalisation ability and robustness.