Improving Segmentation Accuracy for Asphalt Pavement Cracks via Integrated Probability Maps

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.