Robust Vision-Based Daytime Vehicle Brake Light DetectionUsing Two-Stage Deep Learning Model

• Main Authors: Tzu-Yang Lin, 林子揚
• Other Authors: Duan-Yu Chen
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9m435s

碩士 === 元智大學 === 電機工程學系 === 106 === In the modern age of advanced automotive technology and deep learning, most people have owned their own vehicle and each car is more and more equipped. The Advanced Driving Assistance System (ADAS) has gradually become a basic equipment for vehicles. In this environment, the development value of Internet of Vehicle is also increasing. If you can inform the driver of the driving conditions near the vehicle through the Internet of Vehicle, you can avoid most of the accidents. Today's ADAS functions can be divided into active control, early warning and other auxiliary three major categories, including Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Forward Collision Warning (FCW), which uses radar and some sensors to measure the distance between itself and the front, and uses this as a parameter for analysis. However, in addition to the distance, if it is possible to know the information of the vehicle in front of the vehicle in a timely manner and transmit the real-time vehicle information of the vehicle in front through the Internet of Vehicle, it will be possible to more accurately determine the driving conditions of the surrounding vehicles. Therefore, we combined the latest object detection network and classification network and proposed an daytime vehicle brake light detection system that uses a single image as the input and does not require tracking. The screen of the general driving recorder is used as input, and the candidate area of the rear vehicle is found through the first stage of vehicle detection. Then the candidates are put into the second stage of the vehicle brake light recognition network to get the results. The experimental results show that our proposed system can achieve very high resolution under various weather conditions.