Development of the Localized Road Damage Detection Model Using Deep Neural Network

Abstract

Many municipalities and local road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art data collection equipment for collection and analysis of road deficiencies. The paper describes the development of a localized road damage detection model using the transfer learning method and assessment of its usability for training the detection model from a local road image dataset of a limited size. Localized road damage dataset is created by capturing 3,923 Czech and Slovak road images containing 5,072 instances of detected road damage using a smartphone installed on the vehicle's windshield. Then, a supervised neural network was trained using the road damage dataset labeled by experts. A pre-trained MobileNet model developed by the University of Tokyo and transfer learning method were employed to accelerate the training process and to improve the model's performance when a relatively small, localized dataset is used. Finally, the performance of the developed road damage detection model was analyzed. The results show that it is possible to capture road damage into preset classes with accuracy based on the F1-score ranging between 45% and 98%. Further improvement in the detection rate can be achieved by increasing the training dataset size. The developed road damage detection model is publicly available on https://github.com/amraz39/RoadDamage DetectorCZ and it shows the high potential of employing deep neural networks in the detection of road damage by local road agencies.