A Multi-Classifier System for Rock Mass Crack Segmentation Based on Convolutional Neural Networks

Abstract

In rock masses, presence of cracks greatly affects the behavior of it. Obtaining the cracks is very important in specialized analysis of rock mechanics. In computer vision applications, crack segmentation task in an intricate texture such as rock mass, is difficult. Crack segmentation problem can consider as an edge detection task so we can use edge detection methods to achieve it. In this paper, we propose a multi-classifier system based on deep convolutional neural network (CNN) to predict pixel-wise cracks in rock mass images. We provide a dataset consists of 489 RGB rock mass images with manual ground truths. For training classifiers, we create two sub-datasets obtained by mentioned dataset. Also we introduce a new approach of image labeling to improve general methods. Based on the results, our method achieves F-score of 84.0, which has a best performance compared to different methods.