ScholarWorks@숙명여자대학교

초록

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously beingconducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distrationand 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification resultoutput from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracyof classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting theclassification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposedmethod obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which isthe highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurateattention areas were found than the results of the attention area found when only the basic model was used.

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