To address the current challenges of difficult image acquisition and slow detection for bearing defects, a single-station online detection system for full surface defects of bearings based on machine vision has been developed. The system utilizes a line-scan camera in conjunction with an optical system to efficiently capture images of the entire surface of the bearings. The defect detection network model constructed is based on ConvNeXt as the feature extraction backbone, employing feature fusion to supplement feature information, it then undergoes lightweight modifications. Additionally, a multi-task learning training approach is adopted, enabling the model to effectively process feature information at various levels, thereby significantly enhancing its performance in bearing surface defect detection tasks. Experimental results show that compared to ConvNeXt-Tiny, the proposed deep learning model achieves a 4.14% improvement in detection accuracy, reaching 99.02%. When relying on CPU for computation, the average detection time per bearing by the single-station full surface defect online detection system is 0.735 seconds. This system is characterized by its compact size, low cost, and ability to meet the requirements for online detection of bearing surface defects, showcasing promising application prospects.