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http://dx.doi.org/10.12815/kits.2018.17.5.55

Development and Evaluation of Automatic Pothole Detection Using Fully Convolutional Neural Networks  

Chun, Chanjun (Korea Institute of Civil Engineering and Building Technology (KICT))
Shim, Seungbo (Korea Institute of Civil Engineering and Building Technology (KICT))
Kang, Sungmo (Korea Institute of Civil Engineering and Building Technology (KICT))
Ryu, Seung-Ki (Korea Institute of Civil Engineering and Building Technology (KICT))
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.5, 2018 , pp. 55-64 More about this Journal
Abstract
In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.
Keywords
Pothole; Road surface damage; Semantic segmentation; Deep neural network; AI; Fully convolutional neural network;
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