Image-based detection of concrete water tank damage in mining areas holds promise for practical applications. However, current deep learning-based detection algorithms often face challenges in balancing accuracy with computational complexity for real-world deployment. This paper presents an improved YOLOv5 detection method for concrete water tank damage. Firstly, the conventional convolution module in the CSPDarknet backbone is optimized with GSConv (Grouped Shuffle Convolution) to enhance feature extraction while reducing the number of parameters. Secondly, a weight transformation attention mechanism is integrated into the C3 structure to strengthen the feature representation of crack regions. Finally, the Minimum Point Distance IoU (MPDIoU) is employed for precise localization of irregular damage. On a dataset of over 11,000 images, the proposed method achieves a mean average precision (mAP@0.5) of 84.3% (precision: 88.7%; recall: 85.9%). It outperforms the original YOLOv5s, with a 6.5% higher mAP and an 11.1% faster inference speed, while maintaining a compact model size of 7.5M parameters and running at 86 FPS. Ablation studies confirm the individual contributions of each proposed module to these improvements. The algorithm thus provides an efficient and accurate solution that is suitable for deployment on resource-constrained devices.
Ma et al. (2026) studied this question.