AbstractRoad condition and quality are critical road maintenance and risk reduction factors. Most existing road monitoring systems include regular on-site surveys and maintenance. However, major roads in urban areas are generally complicated and have heavy traffic during the daytime, so such field investigation can be significantly limited. Moreover, any road work at nighttime can be risky and dangerous and incur excessive expenses. Based on a review of existing systems for monitoring road conditions, this study focuses on overcoming two unsolved challenges: the capacity of the monitoring range and the avoidance techniques to ensure traffic is not hindered. To solve these challenges, these paper proposes an integrated road monitoring system called Car-free Street Mapping (CfSM) using unmanned aerial vehicles (UAV), aerial mapping cameras, and deep learning (DL) algorithms. The use of the aerial mapping camera mounted on the UAV is to widen the monitoring viewing range, and general-purpose drones are used in this study rather than expensive special equipment. Since the drone-taken images include many passing vehicles that conceal the road surface from the camera vision, the DL model was applied to detect the vehicles and their shadows and then remove them from the images. To train the DL model, two image datasets were used: publicly available cars overhead with context (COWC) images and orthoimages additionally taken for the project to further improve the accuracy. The two datasets consist of 298,623 labeled objects on 9,331 images in total. The tests resulted in a mean average precision (mAP) of 89.57% for trucks, 95.77% for passenger vehicles, and 76.51% for buses. Finally, the object-removed images were composited into one whole car-free image. The CfSM was applied to two areas in Yeouido and Sangam-dong, Seoul, Korea. The car-free images in both regions show a spatial resolution of 10 mm and can be used for various purposes such as road maintenance and management and autonomous vehicle roadmaps.

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