关键词: 深度神经网络, 近景监控场景, 交通监控场景, YOLOv3, 车辆类型, 车辆颜色

Abstract:

Single vehicle attribute recognition cannot meet the existing traffic system. In order to improve the vehicle detection and positioning reliability in actual monitoring, a model is built using the idea of deep neural networks, which can identify vehicle attributes in two different scenarios：close range monitoring scenarios and traffic monitoring scenarios, included vehicle type and color. Based on the YOLOv3 neural network, it is improved to reduce the network depth while ensuring the accuracy rate. The vehicle type and color attributes are graded training, improve model detection speed. In addition, the AttributesCars vehicle attribute dataset is created to complete the data preparation. Experimental results show that the proposed method can meet the real-time requirements of video under the premise of an average accuracy rate of 95.63%, and has achieved good results in two different scenarios, is suitable for multi-scenario vehicle attribute recognition.

Key words: deep neural network, close range monitoring scene, traffic monitoring scene, YOLOv3, vehicle type, vehicle color