基于深度学习的夜间危险驾驶行为检测算法
Detection Algorithm of Dangerous Driving Behavior at Night Based on Deep Learning
唐天俊 1宋平2
作者信息
- 1. 重庆工商职业学院,重庆 401520
- 2. 四川兴蜀工程勘察设计集团有限公司,四川成都 610000
- 折叠
摘要
夜间弱光环境下的危险驾驶行为易导致交通事故的发生,然而,目前多数危险驾驶行为检测算法研究主要集中于光照充足的环境,而在弱光环境下的检测准确率偏低.针对该难点,提出了一种基于深度学习的夜间危险驾驶行为检测算法.该算法由弱光增强模块和检测模块构成.其中,弱光增强模块采用轻量化的零参考深度曲线估计算法提高图像曝光度,检测模块基于NanoDet-Plus模型检测弱光增强处理后的图像是否存在危险驾驶行为.实验结果表明,所提算法在夜间弱光环境下具有较高的检测准确率,同时模型参数量小,检测速度可达毫秒级,可部署在移动设备上进行实时检测.
Abstract
Dangerous driving behavior in low light environment at night is easy to lead to traffic accidents. However,most of the current research on dangerous driving behavior detection algorithms mainly focuses on the environment with sufficient light,and the detection accuracy in low light environment is low. To solve this problem,a deep learning-based night dangerous driving behavior detection algorithm is proposed. The algorithm consists of two modules:the weak light enhancement module and the detection module. Among them,the low-light enhancement module uses a lightweight zero-reference depth curve estimation algorithm to improve image exposure,and the detection module detects whether there is dangerous driving behavior in the images after low-light enhancement based on the NanoDet-Plus model. The experimental results show that the proposed algorithm has high detection accuracy in low light environment at night,and the number of model parameters is small,the detection speed can reach the millisecond level,and it can be deployed on mobile devices for real-time detection.
关键词
深度学习/弱光环境/夜间危险驾驶/行为检测/弱光增强Key words
deep learning/low light environment/dangerous driving behavior at night/behavior detection/weak light enhancement引用本文复制引用
出版年
2024
NETL
NSTL
万方数据