Pothole detection using spatio-temporal saliency

This study proposes a simple and fast pothole detection method of a video using spatio-temporal saliency, where a pothole is a type of failure on an asphalt pavement. In monocular vision, potholes typically have two visual properties. First, potholes have low-intensity areas that are darker than nearby pavement because of shadows. Second, the texture inside the potholes is coarser than the nearby pavement. However, these two visual properties are not sufficient for accurately detecting potholes because of shadows on the pavement and lack of the texture. In this study, the above challenges are addressed by detecting slower objects that are coming closer to a moving vehicle. Pothole is a stationary object ahead on the road. In terms of the relative speed, pothole is a slower object than a moving vehicle. In the video sequence acquired from a dash-cam, different image characteristics appear depending on the relative speed of objects such as potholes and cars ahead. The proposed method detects outliers using spatio-temporal saliency in the yt image: a slower object is represented as diagonal outliers. Experiments with several dash-cam videos show the effectiveness of the proposed method for real video sequences that contain potholes, cars, road lanes, and pavement markings.