Intelligent detection of airport pavement subtle crack based on one-stage anchor-free deep neural network

To solve issues with low-detection accuracy and high-missed detection rate in subtle pavement cracks, this research proposed an intelligent detection method based on a one-stage anchor-free deep neural network, which consists of a feature extraction network, detection neck, and detection head. The feature extraction network with an extended deep attention network is responsible for extracting the features and output at different scales. The detection neck containing the special spatial pyramid structure fuses the contextual information of different scales to improve the abstract features. The detection head is composed of the anchor-free mechanism with decoupling property and the reparameterized structure to improve the detection accuracy and balance the algorithm complexity. The experimental results demonstrate that the Precision, Recall, and F1-Score are, 92.74%, 91.92%, and 0.9233 respectively. It has advantages over the most advanced one-stage detection method. Based on testing results, it has prominent stability and robustness for subtle pavement crack detection.