Real-Time Detection of Cracks in Highway Tunnel Linings Using the Ghost-YOLOv8-SF Method

Tunnel linings are prone to cracking under the influence of various external factors, while manual inspection and traditional digital image-based detection methods are limited by low efficiency and insufficient accuracy. Considering the limitations of existing YOLO models in small object detection, shape adaptation, and lightweight implementation, this study proposes an improved Ghost-YOLOv8-SF model. This model achieves a balanced integration of lightweight characteristics and long-range feature capture capabilities while enhancing small object detection performance. In order to preserve high-frequency features of minute cracks, the model incorporates a WaveletLikeConv module which simulates wavelet transforms through four-branch convolutions. In addition, an ElongatedConv module is designed to adapt to elongated crack morphologies. It employs scenario-based data augmentation strategies and a noise suppression gate module to enhance model generalization and interference resistance. Incorporating GhostNet V2 principles, the backbone network is restructured for lightweight optimization through enhanced Ghost modules, feature subsampling optimization, and GhostV2 bottleneck improvements. Comprehensive experiments are conducted using an integrated multi-scenario dataset. Furthermore, ablation studies are designed to evaluate the performance of the model in terms of detection accuracy, computational efficiency, and real-time capability. Experimental results show that the proposed model improves overall detection accuracy and false-alarm suppression, achieving higher mAP and precision while maintaining practical inference capability. Although the enhanced feature representation introduces additional parameter and computational costs, the model provides a favorable trade-off between detection performance, robustness, and computational efficiency.