Application of Vehicle Detection Based On Deep Learning in Headlight Control

Zixun Huang, Chuin-Mu Wang, Wun-Ciang Wu, Wun-Syun Jhang
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Abstract

When driving at night, vehicle lights are the greatest guarantee for driving safety. Drivers often turn on the high beams to make the oncoming vehicle unclear, turn on the high beams of the oncoming vehicle to reduce their visual range or turn on the high beam when driving behind. The lights prevent themselves from being able to check the rear view through the rear mirror and cause traffic accidents. In addition, drivers often fail to keep a safe distance from the traffic, which causes serial accidents. In response to this problem, this paper proposes a deep learning-based image recognition in the headlight control system. Vehicles using this system can detect the vehicle ahead in real time when the driver is driving the vehicle, and calculate the safety distance of the vehicle ahead, and judge at night Whether there are vehicles in the front and oncoming lanes to determine whether to turn on the high beam, so as to reduce light damage and safe distance traffic accidents.
基于深度学习的车辆检测在车灯控制中的应用
夜间行车时,车灯是行车安全的最大保障。司机经常打开远光灯以使迎面而来的车辆看不清,打开迎面而来的车辆的远光灯以缩小他们的视野范围,或者在后面驾驶时打开远光灯。车灯阻止自己通过后视镜查看后视镜,从而导致交通事故。此外,司机经常不能与车辆保持安全距离,这导致了一系列事故。针对这一问题,本文提出了一种基于深度学习的前照灯控制系统图像识别方法。使用该系统的车辆可以在驾驶员驾驶车辆时实时检测前方车辆,并计算前方车辆的安全距离,并在夜间判断前方和迎面而来的车道是否有车辆,以确定是否打开远光灯,从而减少光损和安全距离交通事故。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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