| Summary: | 碩士 === 國立臺灣師範大學 === 機電科技研究所 === 98 === The rapid development of engineering and technology creates the possibility of traffic flow through long tunnels. However, to obtain the traffic information and incident handling and escape systems in the tunnel, are more difficult than open space in the general highway, hard on a few times, even several hundred times. Therefore, the common user for the special transport space in long tunnel, there is no perfect knowledge, and traffic information. If accident prevention alerts was done before the accident, and really time of traffic information in the tunnel, has become an important issue for a smooth traffic flow or not.
The purpose of this study is using image processing to achieve (1) estimation the traffic information in tunnel, (2) the vehicle spanning double white line detection, (3) the failure and stopped vehicle detection (4) fire detection in long tunnel. Before the accident occurred and after, the appropriate warning signal through the CMS (Changeable Message Sign)with timely, not only can reduce accidents but saving more times in the emergency treatment after the accidents, to make driving more smoothly.
In this study, we used the Double White Line information in long highway tunnel to (1) set a number of vehicles and estimating the traffic volume, (2) detect the vehicle spanning Double White Line while the vehicle intersects with the Double White Line, (3) detect the vehicle failure by using the Temporal-Spatial momentum algorithm, (4) detect flame by analyzing its color space, based on standard long highway tunnel lighting.
As the experimental results, our accuracy of the detection is upper than ninety percentage in each incident detection. And, the traffic with a minimum speed limit as well as standard lighting brightness of the environment in long tunnel, the proposed of (1) temporal-Spatial momentum algorithm to detect the failure and stopped vehicle, and (2) color space used to detect the flame region, to detect traffic incident in the Pakuashan tunnel, the experimental results is earlier 1 second, and 2.6 seconds than the existing image detection methods.
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