A customized multi-class pavement distress segmentation method for routine repair monitoring

This study investigates the efficacy of intelligent detection methods for monitoring the quality of urban road asphalt pavement repairs, and focuses on addressing the challenges of identifying overlapping target pixels and differentiating between fine-scale distress and large-scale block patches, tasks that heavily rely on spatial semantics. A segmentation dataset comprising 13 classes and 5633 labels was constructed. Five semantic segmentation models including Deeplab V3+, SCTNet (Single-Branch Convolutional Neural Network with Transformer Semantic Information), FastFCN (Fast Fully Convolutional Network), MobileNet V3, and SegNext were constructed and evaluated. Despite its relatively smaller size, SCTNet exhibited the highest processing speed, while MobileNetV3 exhibited the smallest size and lowest accuracy. The SegNeXt model demonstrated superior performance in both segmentation accuracy and model complexity, making it chosen as the baseline model. Three training strategies were explored: classifying pavement as background, employing multiscale input, and integrating a weighted loss function. The model that combined all three strategies (referred to as SegNeXt_IPWM) demonstrated the most promising results. Compared to the baseline model, SegNeXt_IPWM achieved significant enhancements, with a 2.08 % increase in mIoU (mean Intersection over Union) and a 1.42 % improvement in mean F-score. Notable improvements were observed across all categories except for marking loss, with particularly substantial gains in loose (+7.75 % IoU), block patch (+6.62 % IoU), and linear crack (+4.87 % IoU) detection. Additionally, SegNeXt_IPWM demonstrated superior generalization capabilities, especially in scenarios involving background target interference, underscoring its potential for robust performance in applications.