A lightweight detection method of pavement potholes based on binocular stereo vision and deep learning

Abstract

The identification of pavement defects is crucial and must be done efficiently, accurately, and cost-effectively. A commercial-grade sports camera and mobile vehicle work together to detect pavement potholes in a lightweight manner. A dataset of 6,186 images with a resolution of 1500 by 1500 pixels has been constructed based on vehicle-mounted tilted images. Object detection and image segmentation are performed using a single-model MASK R-CNN, and the principle of binocular stereo vision is employed to reconstruct the three-dimensional (3D) structures of potholes. A novel methodology is introduced to calculate the 3D feature parameters of potholes, enabling the precise determination of the pavement pothole damage ratio. An average accuracy of 98% for detection and 94% for segmentation is achieved with the Mask R-CNN model. The proposed algorithms achieve an average DR calculation accuracy of 82%, facilitating precise identification of surface irregularities. The paper presents an intelligent approach for detecting pavement potholes.