Crack-DETR: Complex Pavement Crack Detection by Multifrequency Feature Extraction and Fusion

Automated detection of pavement cracks plays a crucial role in road maintenance and traffic safety. However, pavement crack detection under noisy conditions is challenging due to the complex expression forms of pavement cracks and the presence of multitype background noise. In this article, we propose Crack-DETR, an efficient pavement crack detection deep learning network, that can detect complex pavement cracks from complex background noise. In this network, we first propose a multifrequency feature extraction and fusion (MFEF) block to enhance pavement crack features and suppress background noise from both high-frequency and low-frequency perspectives in two stages: multifrequency feature extraction and multifrequency feature fusion. In the multifrequency feature extraction stage, we design a high-frequency attention mechanism to extract high-frequency features from high-level features. Then, we propose a low-frequency attention mechanism to capture low-frequency features of high-level features. In the multifrequency feature fusion stage, we fuse the high- and low-frequency features through intrafrequency feature fusion and cross-frequency feature fusion. Second, we propose an evaluating metric, diagonal intersection over union (DIoU), to enhance the loss function of Crack-DETR, improving the learning effectiveness of the model. Finally, we conduct extensive experiments on three real-world datasets with different scenarios, and the experimental results show that our method can improve the frames per second (FPS) by about 33.4% and mAP@50 by about 6.78% on average compared with baseline model in the YN dataset. Consequently, Crack-DETR achieves higher accuracy while reducing computational complexity, paving the way for the development of sensor-based real-time pavement crack detection.