基于低光增强的夜间疲劳驾驶检测算法

Fatigue Driving Detection Algorithm at Night Based on Low-Light Enhancement

司机疲劳驾车会影响车辆的正常行驶,严重时会威胁司机和乘客的生命安全,因此检测司机是否出现疲劳现象可以有效保障人们的出行安全.在现实生活中,一般在夜间光照强度较弱的情况下,司机出现疲劳驾驶的次数较多,但是现有的相关检测算法无法处理灯光问题,导致其在夜间检测时准确率较低.针对此问题,本文提出了基于低光增强的夜间疲劳驾驶检测算法.首先对人脸图像进行低光增强处理,从而提高图像的曝光度;然后使用人脸关键点检测网络获取图像的眼睛区域;之后使用卷积神经网络对眼睛区域进行睁、闭眼分类;最后统计单位时间内睁、闭眼数量的比值,以此判定司机是否处于疲劳状态.实验结果表明,在夜间环境中,本文提出的检测算法相对现有算法在检测成功率上提升了15.38%,取得了更好的效果.

Driver’s fatigue will affect the normal driving of the vehicle,and in serious cases will threaten the life safety of driver and passengers.Therefore,detecting whether the driver is fatigue can effectively protect people’s travel safety.In real scenario,generally,when the night light intensity is weak,the driver has a lot of time of fatigue driving,but the existing related detection algorithms cannot deal with the lighting problem,resulting in a low accuracy rate at night fatigue driving detection.Aiming at such problem,this study proposed a night-light fatigue driving detection algorithm based on low-light enhancement.Firstly,the LIME algorithm was used to perform low-light enhancement processing on the face image to improve the exposure of the image.Secondly,the face keypoint detection network was used to obtain the eye area of the image.Thirdly,the convolutional neural network was used to classify the eye area with open and closed eyes.Finally,the ratio of the number of eyes opened and closed per unit time is counted to determine whether the driver is in a fatigue state.The experimental results show that in the night environment,the detection algorithm proposed in this study improves the detection success rate by 15.38%compared with the existing algorithms,and achieves better results.