The multi-dimensional defect detection of cylindrical battery shells is a critical technology for ensuring the quality and safety of lithium batteries. Due to the different processes involved in production and transportation, defects may occur in each part of the cylindrical battery shells. To solve the problem of low detection accuracy in existing methods when handling the diverse and variably scaled defects of cylindrical battery shells, this study designs an image acquisition system based on the characteristics of each part of the battery shells and constructs a multi-dimensional defect dataset. Additionally, a multi-dimensional defect detection technology is proposed based on an improved YOLOv8n. Firstly, the switchable atrous convolution is used in the C2f module to improve the multi-scale feature extraction capability. Secondly, the down sampling module is refined by combining average pooling and max pooling strategies, reducing the spatial dimensions of feature maps while retaining key information. Finally, the LSKA attention mechanism is introduced to enhance the fusion effect of multi-scale features. Experimental results show that the improved YOLOv8n model achieves an average detection accuracy of 77.4% on a custom cylindrical battery shell defect dataset, which is 4.3% higher than the original model. Furthermore, the computational load is reduced by 17%, the model size is only 6 MB, and the detection speed reaches 177 FPS, meeting the requirements for real-time industrial mass detection.