HiResCrackNet: high-resolution pavement crack segmentation with hierarchical feature adjustment and multi-scale perception

Abstract Accurate pixel-level segmentation of pavement cracks is an essential measurement task for road safety and infrastructure maintenance, as well as for the quantitative measurement of crack morphology that supports condition assessment and maintenance planning. Although deep learning has advanced crack segmentation, existing methods still struggle to capture long, continuous cracks and delineate irregular edges under complex backgrounds, which limits measurement reliability. To address these challenges, we propose HiResCrackNet, a high-resolution segmentation model designed to enhance both segmentation accuracy and measurement robustness. The model employs a high-resolution backbone to preserve fine-grained features, a hierarchical feature adjuster to balance global structure and local detail, and a multi-scale fusion perception module to strengthen cross-level feature integration. In addition, we release BT-Crack2000, a large-scale dataset with diverse and challenging scenarios to support robust model training and benchmarking. Extensive experiments across multiple datasets demonstrate that HiResCrackNet consistently outperforms state-of-the-art methods, delivering precise crack delineation. These contributions provide a reliable foundation for quantitative pavement evaluation and highlight the potential of intelligent measurement approaches in smart infrastructure management.