Citation

Xie Q, Kwon TJ. Transp. Res. Rec. 2022; 2676(10): 445-459.

Copyright

(Copyright © 2022, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981221090235

PMID

unavailable

Abstract

Road surface condition (RSC) is an important performance indicator for winter road maintenance personnel to maintain safe driving conditions. This becomes more apparent in inclement weather events where timely clearing of snow is highly prioritized. Considering the vast road networks that need to be covered, many transportation agencies have been using camera images to view real-time RSC directly; however, monitoring conditions via these cameras is still being done manually, thereby hindering its full utilization for optimizing maintenance services. Many studies have attempted to develop a deep-learning-based approach known as convolution neural network (CNN) to automate the process of RSC image classification. When implemented, RSC can be extracted from road images without human involvement. However, efforts made thus far have been focused on rural highways, with performance in the urban context being the least explored. Furthermore, CNN models developed in previous studies have been trained either from scratch or via transfer learning, but only a few studies have investigated transfer learning using a pre-trained RSC model. To address these gaps, an urban RSC model was developed in this study via transfer learning using a pre-trained RSC CNN model. The image dataset used contains 3914 urban images collected in a residential area south of Edmonton, Alberta. With these images, the pre-trained RSC model was fine-tuned via transfer learning and underwent hyperparameter optimization to boost performance further, yielding a high classification accuracy of 98.21% and an F1-score of 98.4%, which surpassed the accuracy of the model trained from scratch.

Language: en