Combined Convolutional Neural Networks and Fuzzy Spectral Clustering for Real Time Crack Detection in Tunnels

A computer vision module is proposed for crack detection in tunnels, a challenging process due to the low visibility, the curvature from, and the structures of the cracks which, though being very narrow in width, they are very deep. Our system is embedded on a robot which surveys tunnels in real-time as it is moving in the infrastructure. Initially, a Convolutional Neural Network is employed to detect the cracks which, however, yields only approximate regions due to the great complexity of the scene. Then, a combined fuzzy spectral clustering is then introduced to refine the detected crack regions exploiting spatial and orientation coherency. The algorithms have been tested in real-life tunnels in Egnatia Highway. Our scheme yields high detection accuracy than existing methods and the capacity of the robot to touch the crack to allow in-situ measurements within a precision of 2-3cm in a tunnel of 7m height.