Research on lightweight pipeline defects detection algorithm based on attention mechanism

Abstract The safe operation of underground pipelines is critical for production and daily life. To enhance the effectiveness of pipeline intelligent detection and evaluation, this study proposes a novel lightweight pipeline defect detection algorithm based on attention mechanism, LA-YOLO. Using YOLOv10n as the baseline model, channel and spatial attention mechanisms are incorporated into the backbone network to significantly enhance the model’s capability in extracting target features. The lightweight fasternet block module is introduced to construct the C2f-LF module, replacing the original C2f module to simplify the network structure. A lightweight coordinate attention shared parameters detection head is developed, combining attention mechanisms with shared convolutional technology. This innovation markedly reduces the number of parameters while maintaining detection accuracy. Additionally, Wise-IOU is adopted as the loss function instead of Complete-IoU, further improving the model’s precision. To achieve additional model compression, channel pruning is applied to the LA-YOLO architecture, and knowledge distillation is used to recover potential accuracy loss. Experimental results on the USDID demonstrate that the proposed model maintains comparable accuracy and efficiency to the baseline YOLOv10n, and reduces model size, parameters, and floating-point operations by 76.5%, 76.5%, and 57.8%, respectively. The final model size is only 1.2 MB, highlighting its strong potential for real-world deployment in resource-constrained pipeline inspection systems. This work provides a robust and practical solution for efficient and scalable pipeline defect detection.