基于深度学习的驾驶员分心行为识别

Driver Distracted Behavior Recognition Based on Deep Learning

分心驾驶行为识别是提高驾驶安全的主要方法之一。针对分心驾驶行为识别精度低的问题,本文提出一种基于深度学习的驾驶员分心行为识别算法,由目标检测网络和行为精确识别网络级联构成。基于State Farm公开数据集,第一级利用目标检测算法SSD(Single Shot Multibox Detector)对数据集中的驾驶员原始图像进行局部信息提取,确定行为识别候选区域;第二级分别利用迁移学习VGG19、Res Net50和MobileNetV2模型对候选区域内的行为信息进行精确识别;最后,实验对比级联架构与单模型架构对分心驾驶行为的识别精度。结果表明,提出的级联网络模型相较于主流单模型检测方法,驾驶员行为识别的准确率总体上提升4~7%个百分点。该算法不仅减少噪声和其他背景区域对模型的影响,提高分心行为识别准确率,还可以有效识别更多的行为类别以避免动作的误分类。

Distracted driving behavior recognition is one of the main methods to improve driving safety.Aiming at the problem of low identification accuracy of distracted driving behavior,this paper proposes a driver distracted behavior recognition algorithm based on deep learning,which is composed of a cascade of target detection network and precise behavior recognition network.Based on the State Farm open data set,in the first level,the target detection algorithm SSD( Single Shot Multibox Detector) is used to extract local information from the original driver images in the data set and determine the candidate regions for behavior recognition.Then in the second level,the transfer learning VGG19,Res Net50 and MobileNetV2 models is used to accurately identify the behavior information in the candidate region.Finally,the experiment compares the recognition accuracy of distracted driving behavior between layered recognition architecture and single model architecture.Results show that compared the proposed cascade network model with the mainstream model of single detection method,the driver behavior identification accuracy is improved 4% ~ 7% overall.Besides,the proposed algorithm not only reduces the influence of noise and other background regions on the model to improve the accuracy of distracted behavior recognition,but also can effectively identify more behavior categories to avoid the misclassification of actions.