FALW-YOLOv8: A Lightweight Model for Detecting Pipeline Defects

Pipelines are critical infrastructures in both industrial production and daily life. However, defects frequently arise due to environmental and manufacturing factors, which may lead to severe safety risks. To overcome the limitations of traditional object detection methods, such as inefficient feature extraction and the loss of critical information, this paper proposes an improved algorithm, termed FALW-YOLOv8, built upon the YOLOv8 architecture. Specifically, the FasterBlock is incorporated into the C2f module to replace standard convolutional layers, effectively reducing computational redundancy while improving feature extraction efficiency. In addition, the ADown module is employed to enhance multi-scale feature preservation, while the LSKA attention mechanism is introduced to improve detection accuracy, particularly for small defects. The Wise-IoU v2 loss function is further adopted to refine bounding box regression for complex samples. Experimental results demonstrate that the proposed FALW-YOLOv8 achieves a 5.8% improvement in mAP50, along with a 34.8% reduction in model parameters and a 30.86% decrease in computational cost. These results indicate that the proposed method achieves a favorable balance between accuracy and efficiency, making it well-suited for real-time industrial pipeline inspection applications.