A rapid and precise detection and measurement method for road cracks based on video keyframes and DPPNet

• Efficient video keyframe extraction algorithm based on multi-scale ridge feature constraints of road cracks, providing support for rapid identification of road cracks. • A precise semantic segmentation model for road cracks in video images was established by aggregating global and local features, achieving accurate detection of road cracks in video images in complex dynamic environments. • High precision geometric information acquisition of road cracks based on skeleton feature constraints. Video detection must address issues of low accuracy, slow speed, and inability to accurately capture road crack information. To tackle these challenges, this article proposes a fast detection and measurement method for road cracks using video keyframes and DPPNet. First, a keyframe index space is designed based on multi-scale crack ridge features and change detection to enable rapid keyframe selection. In a second stage, by analyzing complex and dynamic detection scenarios, a high-precision semantic segmentation DPPNet is developed, incorporating dense partial convolution and pyramid modules. This design focuses on local aggregation of crack features across spatial and channel dimensions while reducing feature map redundancy. Finally, a method for measuring crack width in dynamic video under complex conditions is proposed. Results show that our approach outperforms existing methods according to standard indicators, Dice and MIOU have improved by 2.16 %–11.48 % and 1.17 %–5.58 % respectively, which can effectively improve the detection speed by 5–7 times, and meets millimeter-level width detection requirements.