Road Damage Detection and Classification with Faster R-CNN

Abstract

This technical paper presents the method that we use in the Road Damage Detection and Classification Challenge, which is designed to detect damages contained in road images photographed by a vehicle-mounted smartphone. In this task, we apply Faster R-CNN to detect and classify damaged roads. Through analyses of aspect ratios and sizes of the damaged areas in the training dataset, we adjust relevant parameters of the model. In order to solve the problem of unbalanced data distribution of different classes, we introduce some data augmentation techniques (contrast transformation, brightness adjustment, and Gaussian blur) before training. Experimental results demonstrate that our method can achieve a Mean F1-Score of 0.6255 in the competition. The source code and model are publicly available at https://github.com/zhezheey/tf-faster-rcnn-rddc.