Pavement crack detection with hybrid-window attentive vision transformers

Abstract

Pavement, as a kind of common public transit infrastructure, plays an important part in the daily passing and transportation-associated activities. The good conditions and smooth traffics of pavements matter significantly to the pavement users. However, due to long-time services, pavements often suffer from different kinds and severities of distresses, which might bring inconvenience to the pavement-related events, or even cause terrible traffic hazards. In this regard, we put forward a novel hybrid-window attentive vision transformer framework, called CrackFormer, for pavement crack detection aiming at providing an effective and automated solution to serving the pavement distress inspecting and repairing works. The CrackFormer employs a transformer-based high-resolution network architecture to rationally exploit and fuse multiscale feature semantics. To be specific, a hybrid-window based self-attention scheme is designed to extract feature semantics of entities both locally with dense windows and globally with sparse windows, which effectively improves the semantic details and accuracies. Moreover, a weighted multi-head self-attention philosophy is developed to recalibrate the contributions of different heads according to their relevance, which well enhances the feature encoding robustness and saliency. The CrackFormer is systematically tested on seven pavement crack detection datasets. Quantitative evaluations show that the CrackFormer achieves an overall performance with the precision of 0.9376, recall of 0.9352, and F1-score of 0.9364, respectively. In addition, qualitative examinations and comparative analyses all confirm the excellent performance of the CrackFormer for recognizing and delineating the pavement cracks of varying patterns under diverse pavement surface conditions.

Keywords

• Crack detection
• Pavement distress
• Attentive vision transformer
• Feature attention
• High-resolution network
• Deep learning