Roads are the foundation of intelligent transportation systems, yet cracks are widely present in roads and seriously affect system performance. Cracks not repaired promptly can develop into severe road defects, significantly increasing the risk of traffic accidents. Researchers in the community have started to focus on the automatic sensing of cracks in asphalt pavements, while it is still a challenging task on concrete pavements. Cracks in the concrete pavement are easily recognized as interrupted segments rather than a continuous whole due to the interference of the surface texture. This mistake can seriously mislead the judgment of cracks and subsequent road repair. In this paper, we aim to solve the challenge by enhancing contextual information about cracks within the images. We first extract the information from the local and global representations using image information and then fuse it into complete contextual information by a designed multilayer perceptron. Finally, we use the discriminative loss to constrain the edges of cracks and backgrounds using complete crack contextual information. We have collected and annotated several images of concrete pavements from several significant provinces in China. Experiments show that our method achieves the best performance compared to state-of-the-art methods, especially in edge determination.

Road surface deterioration, such as cracks and potholes, poses a significant threat to both road safety and infrastructure longevity. Swift and accurate detection of these issues is crucial for timely maintenance and user security. However, current techniques often overlook the unique characteristics of pavement images, where the small distressed areas are vastly outnumbered by the background. In response, we propose an innovative road distress classification model that capitalizes on sparse perception. Our method introduces a sparse feature extraction module using dilated convolution, tailored to capture and combine sparse features of different scales from the image. To further enhance our model, we design a specialized loss function rooted in domain-specific knowledge about pavement distress. This loss function enforces sparsity during feature extraction, guiding the model to align precisely with the sparse distribution of target features. We validate the strength and effectiveness of our model through comprehensive evaluations of a diverse dataset of road images containing various distress types and conditions. Our approach exhibits significant potential in advancing traffic safety by enabling more efficient and accurate detection and classification of road distress.