Improved Zero-DCE Low-Light Enhancement Algorithm for Night Fatigue Driving Detection
HUANG Zhenyu1,2, CHEN Yutao1,2,3, LIN Dingci1,2, HUANG Jie1,2,3
1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108; 2. 5G+ Industrial Internet Institute, Fuzhou University, Fuzhou 350108; 3. Key Laboratory of Industrial Automation Control Technology and Information Processing of Fujian Province, Fuzhou University, Fuzhou 350108
摘要 为了提高夜间疲劳驾驶检测的准确率,在现有低光增强算法Zero-DCE(Zero-Reference Deep Curve Estimation)的基础上,提出改进Zero-DCE的低光增强算法.首先,引入上下采样结构,减少噪声影响.同时,引入注意力门控机制,提高网络对图像中人脸区域的敏感性,有效提高网络的检测率.然后,针对噪声相关问题,提出改进的核选择模块.进一步,使用MobileNet的深度可分离卷积替换Zero-DCE的标准卷积,提高网络的检测速度.最后,通过人脸关键点检测网络和分类网络,判断驾驶员的疲劳状态.实验表明,在夜间环境下,相比现有的疲劳驾驶检测算法,文中算法在人脸检测的准确率和眼睛状态的识别率上都有所提升,取得较令人满意的检测效果.
Abstract：Grounded on the existing low-light enhancement algorithm, zero-reference deep curve estimation(Zero-DCE), an improved Zero-DCE low-light enhancement algorithm is proposed to increase the accuracy of night fatigue driving detection. Firstly, the upper and lower sampling structure is introduced to reduce the influence of noise. Secondly, the attention gating mechanism is employed to improve the sensitivity of the network to the face region in the image, and thus the detection rate is increased effectively. Then, an improved kernel selecting module is proposed for the problems arising from noise. Furthermore, standard convolution of Zero-DCE is replaced by the depthwise separable convolution of MobileNet to accelerate the detection. Finally, the driver fatigue state can be judged by the face key point detection network and classification network. The experimental results show that the proposed algorithm improves the accuracies of face detection and eye state recognition rate in a night environment with satisfactory detection results compared with the existing fatigue driving detection algorithms.
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2022, Vol. 35 
