CDCL-VRE: An ensemble deep learning-based model for distracted driver behavior detection

被引:0
|
作者
Sun, Haibin [1 ]
Li, Zheng [1 ]
机构
[1] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, 579 Qianwangang Rd, Qingdao 266590, Peoples R China
关键词
Deep learning; neural networks; distracted behavior; ensemble learning; semantic segmentation; NEURAL-NETWORKS;
D O I
10.3233/JIFS-234593
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Millions of traffic accidents occur worldwide each year, resulting in tens of thousands of deaths. The primary cause is the distracted behavior of drivers during the driving process. If the distracted behaviors of drivers during driving can be detected and recognized in time, drivers can regulate their driving and the goal of reducing the number of traffic fatalities can be achieved. A deep learning model is proposed to detect driver distractions in this paper. The model can identify ten behaviors including one normal driving behavior and nine distracted driving behaviors. The proposed model consists of two modules. In the first module, the cross-domain complementary learning (CDCL) algorithm is used to detect driver body parts in the input images, which reduces the impact of environmental factors in vehicles on the convolutional neural network. Then the output images of the first module are sent to the second module. The Resnet50 and Vanilla networks are ensembled in the second module, and then the driver behavior can be classified. The ensemble architecture used in the second module can reduce the sensitivity of only a single network on the data, and then the detection accuracy can be improved. Through the experiments, it can be seen that the proposed model in this paper can achieve an average accuracy of 99.0%.
引用
收藏
页码:2759 / 2773
页数:15
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