RPDNet: Street-level road pavement damage detection with a real-time anchor-free network

Accurately and timely detecting road pavement damage helps monitor road deterioration extent, thereby guiding maintenance projects and ensuring traffic safety. Nevertheless, due to textural similarity and nested distribution between neighboring pavement damages, as well as the damages with the diversity sizes, irregular shapes, multiple categories, current methods have the limitation in the high-quality detection from road street-level images. To tackle these challenges, this paper develops a novel real-time anchor-free network with a one-stage processing architecture, named RPDNet, for precisely and accurately detecting pavement damages from streel-level road images. First, stacked with a layer-by-layer encoding structure boosted by a deformable fully-attentive module as the backbone extractor, the RPDNet can capture more fine-grained information and generate multiscale strong task-aware semantics, favoring significantly the discrimination noteworthy textural and geometric features. Then, by adopting a multi-level efficient aggregation neck, the RPDNet can promote informative spatial details and integrate the different-level damage encoding features, contributing to the light-weight and optimization of the whole architecture. Afterward, designed with a dual-large kernel module, embedded in a decoupled detection head with anchor-free guidance, the RPDNet can project the ranging dependency of salient and task-oriented pavement damage objects by adaptively aggregating information across large kernels in spatial-domain. Qualitative and quantitative evaluations confirmed that the RPDNet provided a promiseful solution for detecting pavement damages in industrial applications under complex street-level road conditions. Furthermore, comparative analysis with the latest anchor-based and anchor-free alternatives also proved the superiority and generalization of the RPDNet in pavement damage detection tasks. The assessment results displayed that the RPDNet obtained an average mAP@0.5 , mAP@0.5:0.95 , precision , and recall of 69.16%, 44.86%, 72.59%, and 60.41%, respectively, on two dataset. Additionally, we constructed a large-size multi-city road pavement damage image dataset to support urban road health monitoring.