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
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.
Fund:National Natural Science Foundation of China(No.61603094)
Corresponding Authors:
HUANG Jie, Ph.D., professor. His research interests include pa-ttern recognition, intelligent systems, multi-agent systems and 5G+ industrial internet.
About author:: HUANG Zhenyu, master student. His research interests include image processing and pattern recognition.CHEN Yutao, Ph.D., associate professor. His research interests include model predictive control algorithms, hybrid enhanced intelligence, unmanned intelligent systems and applications.LIN Dingci, master, senior engineer. His research interests include digital economy technology, natural language semantic understanding, robot control and human-machine interaction.
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2022, Vol. 35 
