Automatic Real-Time Road Crack Identification System

Crack identification is a common problem that requires human involvement and manual identification. Our work is focused on detecting street surface cracks using Computer Vision algorithms. The problem at hand has been divided in three steps: (i) the first step transforms a given 3D video in 2D individual frames, (ii) the second step processes these frames in order to identify the relevant part of the street using Support Vector Machine and Vanishing Point Detection and (iii) in the third step the detection itself has been implemented using three methods: Convolutional Neural Network, U-Net and a Local Binary Pattern. In this paper we present our methods, experiments and results for detecting cracks on surfaces like streets and sidewalks.