The accurate detection of pavement surface distress is crucial for timely maintenance and prevention of traffic accidents. Traditional deep-learning techniques, while effective, rely heavily on supervised learning, necessitating extensive and costly annotated datasets. This paper presents CL-PSDD(Contrastive Learning for Adaptive Generalized Pavement Surface Distress Detection), a novel unsupervised contrastive learning approach that overcomes these limitations by capturing robust feature representations of pavement surface distress across various conditions without manual labeling. We introduce a synergistic methodology that combines SwAV for effective unsupervised learning and YOLO for efficient object detection, enabling the model to learn intricate feature representations of pavement surface distress. Furthermore, we propose RE-RPN-K(Road Enhanced Region Proposal Network in K), a specialized region proposal network that distills precise road surface information, and integrates a transformer-based DETR-Head, eliminating the need for conventional NMS(Non-Maximum Suppression) post-processing. Our experiments on the GRDDC and IRRDD datasets demonstrate that CL-PSDD achieves competitive mean Average Precision (mAP) scores of 76% and 60%, respectively, rivaling the performance of supervised methods and excelling in generalization. This study pioneers the application of unsupervised learning to extensive pavement surface distress detection, establishing a significant milestone and providing a valuable reference for future research in this vital field.
CL-PSDD: Contrastive Learning for Adaptive Generalized Pavement Surface Distress Detection
IEEE Transactions on Intelligent Transportation Systems ; 26 , 4 ; 5211-5224
2025-04-01
3839709 byte
Article (Journal)
Electronic Resource
English
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