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We propose a mobile system, called PotholeEye + , for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye +  pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye +  on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye +  detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of 110 km/h on a real highway.

PotholeEye + : Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance