Large-scale, fully automated, and comprehensive spatiotemporal pavement crack evaluation incorporating geographic information system, street view images, deep learning, and cluster analysis

Abstract

Automatic condition assessment of road pavements is important for efficient pavement management. Most previous studies targeted highway, national, and state road routes and adopted their own imaging vehicles to evaluate pavement conditions. This study focuses on street view images for large-scale, fully automated, and comprehensive condition evaluation including local municipality roads. This study proposes a low-cost, efficient, and accurate method combining geographic information system (GIS), street view images, and state-of-the-art deep learning and clustering methods. The three contributions are: (1) Automatic high-quality data acquisition method is established. Geographic positions and road directions are estimated by image processing of GIS maps. Google Cloud Application Programming Interface is adopted. Street view images are screened to remove interior and building façade images applying a road segmentation U-Net trained with the CityScapes dataset. (2) Previous road damage dataset RDD2020 is augmented to adjust to street view images with shadows from adjacent objects, walls, pedestrian road tiles, and logos. You only look once version 8 (YOLOv8) is adopted to detect damages and classify the conditions at each location. (3) It was first revealed at a fine local scale in large administrative areas that the damages show spatial cluster patterns on GIS maps. Time histories are analyzed to depict deterioration process. To validate the method, about 144,000 images were collected in four wards in the Tokyo districts. It costs $0 to $100 and 5 to 10 h for one ward. A fine-tuned YOLOv8 model achieved about 95% classification accuracy. Damage maps varied while curves were similar, which are effective in practice, reflecting each municipality's pavement condition and meeting the inspection standards.