Spatial Adaptive Improvement Detection Network for Corroded Bolt Detection in Tunnels

Abstract

The detection of corroded bolts is crucial for tunnel safety. However, the specific directionality and complex texture of corroded bolt defects make current YOLO series models unable to identify them accurately. This study proposes a spatial adaptive improved detection network (SAIDN), which integrates a spatial adaptive improvement module (SAIM) that adaptively emphasizes important features and reduces interference, enhancing detection accuracy. The SAIM performs a detailed analysis and transformation of features in the spatial and channel dimensions, enhancing the model’s ability to recognize critical defect information. The use of depthwise separable convolutions and adaptive feature reweighting strategies improves detail processing capabilities and computational efficiency. Experimental results show that SAIDN significantly outperforms existing models in detection accuracy, achieving 94.4% accuracy and 98.5% recall, surpassing advanced models such as YOLOv9 and Cascade RCNN. These findings highlight the potential of SAIDN in enhancing subway tunnels’ safety and maintenance efficiency.