DFL-YOLO: A tunnel crack detection algorithm for feature aggregation in complex scenarios

Tunnel crack detection is a critical application area in computer vision. However, existing detection algorithms face challenges such as low recognition rates, slow detection speeds and high costs in complex tunnel environments. To address these issues, we propose a fast tunnel crack detection algorithm model based on YOLOv8n, named DFL-YOLO. This model introduces a novel Feature Aggregation Pyramid Network (FAPN) that effectively handles variable textures and improves the detection success rate of small cracks. It also includes a Lightweight Detail-Enhanced Shared Convolution Detection Head (LDSC), which reduces the number of parameters and detection time through shared convolution calculations, thereby enhancing detection efficiency. Additionally, Detail-Enhanced convolutional composition C2f-DEConv module is incorporated into the C2f structure to enhance the crack feature extraction capability of the backbone network and thus improve the robustness. The improved DFL-YOLO model achieves an accuracy rate of 93.1%, a recall rate of 85% and mAP@50 of 91.7% for tunnel crack detection, while its computational intensity is 43.8% lower than that of the original YOLOv8 model. This makes DFL-YOLO more suitable for deployment on resource-constrained detection devices.