STCYOLO: Subway Tunnel Crack Detection Model with Complex Scenarios

The detection of tunnel cracks plays a vital role in ensuring structural integrity and driving safety. However, tunnel environments present significant challenges for crack detection, such as uneven lighting and shadow occlusion, which can obscure surface features and reduce detection accuracy. To address these challenges, this paper proposes a novel crack detection network named STCYOLO. First, a dynamic snake convolution (DSConv) mechanism is introduced to adaptively adjust the shape and size of convolutional kernels, allowing them to better align with the elongated and irregular geometry of cracks, thereby enhancing performance under challenging lighting conditions. To mitigate the impact of shadow occlusion, a Shadow Occlusion-Aware Attention (SOAA) module is designed to enhance the network’s ability to identify cracks hidden in shadowed regions. Additionally, a tiny crack upsampling (TCU) module is proposed, which reorganizes convolution kernels to more effectively preserve fine-grained spatial details during upsampling, thereby improving the detection of small and subtle cracks. The experimental results demonstrate that, compared to YOLOv8, our proposed method achieves a 2.85% improvement in mAP and a 3.02% increase in the F score on the crack detection dataset.