Abstract
This paper proposes a real-time detection method for a car driving ahead in real time on a tunnel road. Unlike the general road environment, the tunnel environment is irregular and has significantly lower illumination, including tunnel lighting and light reflected from driving vehicles. The environmental restrictions are large owing to pollution by vehicle exhaust gas. In the proposed method, a real-time detection method is used for vehicles in tunnel images learned in advance using deep learning techniques. To detect the vehicle region in the tunnel environment, brightness smoothing and noise removal processes are carried out. The vehicle region is learned after generating a learning image using the ground-truth method. The YOLO v2 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments. The vehicle detection rate is approximately 87%, while the detection accuracy is approximately 94% for the proposed method applied to various tunnel road environments.
Highlights
Various technologies for autonomous vehicles have emerged
The road environment information is analyzed through visual information, the situation is recognized, and the vehicle steering task is determined through the driving task
We propose a method for the real-time detection of vehicles in vehicle black-box images acquired on tunnel roads
Summary
Various technologies for autonomous vehicles have emerged. A support system for the safe driving of vehicles has been achieved by combining various sensors included in the vehicle, such as lane maintenance, omnidirectional vehicle distance estimation, side vehicle detection, and vehicle distance maintenance sensors [1,2,3]. 2019 in Figure the ratio of vehicle-to-vehicle traffic accidents was to and status of each vehicle among in tunnels
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