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(사)한국터널지하공간학회

ISSN : 2233-8292

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Vol.19 No.6

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Development of a deep-learning based tunnel incident detection system on CCTVs

(사)한국터널지하공간학회 / (사)한국터널지하공간학회, (P)2233-8292; (E)2287-4747
2017, v.19 no.6, pp.915-936
https://doi.org/10.9711/KTAJ.2017.19.6.915




& (2017). Development of a deep-learning based tunnel incident detection system on CCTVs. (사)한국터널지하공간학회, 19(6), 915-936, https://doi.org/10.9711/KTAJ.2017.19.6.915

Abstract

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

keywords
터널 영상유고감지 시스템, 딥러닝 알고리즘, 터널 CCTV, 영상처리, 터널 객체 영상 빅데이터, Automatic tunnel incident detection system, Deep learning algorithm, Tunnel CCTV, Image processing, Tunnel object image big data

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