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(사)한국터널지하공간학회

ISSN : 2233-8292

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Vol.20 No.6
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Development of a deep-learning based automatic tracking of moving vehicles and incident detection processes on tunnels

(사)한국터널지하공간학회 / (사)한국터널지하공간학회, (P)2233-8292; (E)2287-4747
2018, v.20 no.6, pp.1161-1175
https://doi.org/10.9711/KTAJ.2018.20.6.1161



& (2018). Development of a deep-learning based automatic tracking of moving vehicles and incident detection processes on tunnels. (사)한국터널지하공간학회, 20(6), 1161-1175, https://doi.org/10.9711/KTAJ.2018.20.6.1161
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Abstract

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for ‘person’ object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

keywords
Deep learning training and inference, Object detection, Object tracking, Full-cycle system, Tunnel automatic incident detection, 딥러닝 학습 및 추론, 객체 인식, 객체 추적, 전주기 시스템, 터널 유고상황 자동 감지

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