Pavement distress detection using random decision forests

Abstract

Pavement distress detection is a key technology to evaluate pavement surface and crack severity. However, there are many challenging problems when using pavement distress detection technology to do road maintenance, such as the inference of textured surroundings with similar intensity to the distresses, the existence of intensity inhomogeneity along the distresses and the requirement of real-time detection in practice. To address these problems, we propose a novel method for pavement distress detection based on random decision forests. By introducing the color gradient features at multiple scales commonly used in contour detection, we extend the feature set of traditional distress detection methods and get the represented crack with richer information. During the process of training, we apply a subsampling strategy at each node to maintain the diversity of trees. With this work, we finally solve all the three problems mentioned above. In addition, according to the characteristics of random decision forests, our method is easy to parallel and able to conduct real-time detection. Experimental results show that our approach is faster and more accurate than existing methods.