著者

Takafumi KAWASAKI Makoto KAWANO Takeshi IWAMOTO Michito MATSUMOTO Takuro YONEZAWA Jin NAKAZAWA Hideyuki TOKUDA

出版者

公益社団法人 計測自動制御学会

雑誌

SICE Journal of Control, Measurement, and System Integration (ISSN:18824889)

巻号頁・発行日

vol.10, no.5, pp.393-401, 2017 (Released:2017-09-23)

参考文献数

27

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Road maintenance requires local city governments to dedicate a substantial amount of funds in finding and repairing damaged traffic marks and pavements. In developed cities, the total road length is so large that the cost becomes unreasonably high. In this paper, we propose a method of sensing damaged traffic marks from images captured by a camera mounted to a car, for the purpose of reducing road maintenance cost. In particular, we utilized convolutional neural networks (CNN), as well as linear support vector machines (SVM) and Random Forest, in developing a system of damage detection. The experiments used thousands of images captured in the wild and showed that the method can detect damages using CNN with 93% accuracy, at maximum, and at reasonable speed (55 images per second).