GSB-YOLO: An Enhanced Lightweight Model for Robust Road Crack Detection with Multi-Scale Feature Fusion in Complex Environments

Timely detection and regular maintenance of road cracks are critical for road and traffic safety. However, existing detection methods face challenges such as varying target scales, large model parameters, and poor adaptability to complex backgrounds. To address these issues, this study proposes an enhanced GSB-YOLO model. Inspired by the concepts of linear transformation and long-range attention mechanisms, a lightweight network structure was designed to reduce model parameters in the backbone network, thereby improving detection efficiency. Additionally, a novel SMC2f module was introduced in the neck structure, which calculates the "energy" of each neuron in the feature map, evaluates its contribution to the detection task, and dynamically assigns weighted coefficients. This method enhances the model's detection robustness in complex backgrounds and effectively addresses the issue of insufficient emphasis on positive samples. Furthermore, through the optimization of the Path Aggregation Network (PAN) and the Bidirectional Feature Pyramid Network (BiFPN), efficient multi-scale feature fusion is achieved, further strengthening the model's capacity to represent crack features at various scales. Experimental results indicate that the proposed GSB-YOLO model improves the mean average precision (mAP) in road crack detection tasks by 3.2%, demonstrating its significant application value in road crack detection and traffic safety assurance.