Affiliation:
1. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, T6G 2W2, Canada
Abstract
Convolutional neural network (CNN) is a promising image recognition technique for winter road surface condition (RSC), a measure that is crucial for winter maintenance operations. In the past, researchers have designed RSC CNN models that displayed acceptable results but did so focusing solely on obtaining high classification accuracy without any consideration for efficiency. Furthermore, when it comes to model development itself, architecture design requires expertise in CNN as well as rich knowledge in the investigated problem itself. To rectify these issues, this paper proposes an innovative approach to automatically design RSC CNN architecture without compromising classification accuracy. The proposed approach uses a weighted sum method, which provides the freedom of choosing relative importance level between accuracy and efficiency. Once the relative importance has been set, one of the most successful and widely adopted heuristics, namely, simulated annealing (SA), is employed to generate (sub)optimal solutions. Results show that both accuracy and efficiency of the automatically generated CNNs are better or at least comparable to the two selected state-of-the-art CNN models, ResNet50 and MobileNet, achieving as high as 93.44% classification accuracy. Ultimately, the outcome of this study fills the gap in existing CNN design methods that do not consider the tradeoff between accuracy and efficiency while providing insight into the effect varying architectures have on CNN model performance.
Subject
Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering
Reference37 articles.
1. An enhanced spatial statistical method for continuous monitoring of winter road surface conditions;L. Gu;Canadian Journal of Civil Engineering,2019
2. Effect of Winter Weather and Road Surface Conditions on Macroscopic Traffic Parameters
3. Spatial mapping of winter road surface conditions via hybrid geostatistical techniques;M. Wu
4. Convolutional neural network;P. Kim,2017
5. Comparison of deep learning models for determining road surface condition from roadside camera images and weather data;J. Carrillo
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献