Grid-based Pavement Crack Analysis Using Deep Learning

Pavement crack detected plays an important role in pavement maintenance. Image recognition is a traditional way for pavement crack detected. Recently, deep learning is a state-of-the-art method for target detection. CNN (convolutional neural network), a significant method in deep learning, is widely used in image target detection and brings about breakthroughs. However, CNN has not been applied to pavement crack detection. In this paper, we apply CNN to detect pavement crack and PCA (Principal Component Analysis) to classify the detected pavement cracks. Firstly, two databases are obtained by using two different scales of grid (32x32, 64x64) to segment pavement images. Each database has 30000 images for training set. We obtain two kinds of trained CNN. Each CNN is trained by one training set, which is part of each scale databases. We use trained CNN to detect the existence of pavement crack in corresponding scale grids. We confirm the scale of segment grid by comparing the results of pavement crack detected. Secondly, we only keep the grids containing crack and achieve the skeleton of crack in a pavement image. Lastly, we use PCA to analyse the skeleton of crack. The classification of crack can be obtained. The F-measure for crack detection is 94.7%. Meanwhile, the proposed method achieves 97.2%, 97.6% and 90.1% correct rate of classification for longitudinal crack, transverse crack and alligator crack, respectively. The results show proposed method can detect the pavement crack and evaluate the type of crack precisely.