中圖分類(lèi)號(hào)：TP181
文獻(xiàn)標(biāo)識(shí)碼：A
DOI： 10.19358/j.issn.2096-5133.2020.03.012
引用格式：查偉偉,白天.公路視頻實(shí)時(shí)車(chē)輛檢測(cè)分類(lèi)與車(chē)流量統(tǒng)計(jì)算法［J］.信息技術(shù)與網(wǎng)絡(luò)安全，2020,39（3）：6267,72.

Highway video real-time vehicle detection classification and traffic flow statistics algorithm

Abstract： Realtime vehicle detection,classification and traffic statistics based on road video are classic problems in the field of computer vision.The traditional method of setting the detection belt is prone to missed inspection and reinspection,so the automation performance is not good.The realtime performance of onestage algorithm based on deep network can be guaranteed,but the changing background,moving nonvehicle objects are often included,and the change of illumination is sensitive at the same time,so the classification at night is not good.Therefore,an algorithm is proposed to perform target detection by onestage,and the classification result is not directly obtained. Instead, it cuts out the object according to the bounding box,removes the background, and improves resistance to background disturbances and classification accuracy.Then it is sent to a transfer learning shallow neural network. The classified output and the position output of the target detection network are combined and sent to a full map matching algorithm for traffic flow statistics.While ensuring realtime performance,the rate of missed inspections and reinspections is reduced.

Key words : convolutional neural network;target detection and classification;realtime traffic statistics;YOLOv3 network