Improving Segmentation Accuracy for Asphalt Pavement Cracks via Integrated Probability Maps

Abstract

Asphalt crack segmentation is essential for preventive maintenance but is sensitive to noise, viewpoint, and illumination. This study evaluates a minimally invasive strategy that augments standard RGB input with an auxiliary fourth channel—a crack-probability map generated by a multi-scale ensemble of classifiers—and injects it into segmentation backbones. Field imagery from unmanned aerial vehicles and action cameras was used to train and compare U-Net, ENet, HRNet, and DeepLabV3+ under unified settings; the probability map was produced by an ensemble of lightweight convolutional neural networks (CNNs). Across models, the four-channel configuration improved performance over three-channel baselines; for DeepLabV3+, the Intersection over Union (IoU) increased by 6.41%. Transformer-based classifiers, despite strong accuracy, proved less effective and slower than lightweight CNNs for probability-map generation; the final ensemble processed images in approximately 0.63 s each. Integrating ensemble-derived probability maps yielded consistent gains, with the best four-channel CNNs surpassing YOLO11x-seg and Transformer baselines while remaining practical. This study presents a systematic evaluation showing that probability maps from classifier ensembles can serve as an auxiliary channel to improve segmentation of asphalt pavement cracks, providing a novel modular complement or alternative to attention mechanisms. The findings demonstrate a practical and effective strategy for enhancing automated pavement monitoring.