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http://dx.doi.org/10.12815/kits.2019.18.3.106

A Selection Method of Backbone Network through Multi-Classification Deep Neural Network Evaluation of Road Surface Damage Images  

Shim, Seungbo (Korea Institute of Civil Engineering and Building Technology)
Song, Young Eun (Hoseo University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.18, no.3, 2019 , pp. 106-118 More about this Journal
Abstract
In recent years, research and development on image object recognition using artificial intelligence have been actively carried out, and it is expected to be used for road maintenance. Among them, artificial intelligence models for object detection of road surface are continuously introduced. In order to develop such object recognition algorithms, a backbone network that extracts feature maps is essential. In this paper, we will discuss how to select the appropriate neural network. To accomplish it, we compared with 4 different deep neural networks using 6,000 road surface damage images. Based on three evaluation methods for analyzing characteristics of neural networks, we propose a method to determine optimal neural networks. In addition, we improved the performance through optimal tuning of hyper-parameters, and finally developed a light backbone network that can achieve 85.9% accuracy of road surface damage classification.
Keywords
Road Surface Damage; Deep Learning; Maintenance; Image Classification; Backbone Network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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