公路视频实时车辆检测分类与车流量统计算法
2020年信息技术与网络安全第3期
查伟伟,白天
(中国科学技术大学 软件学院,安徽 合肥 230000)
摘要： 公路视频实时车辆检测分类与车流量统计是计算机视觉领域的一个经典问题。传统设置检测带法，易漏检复检，自动化性不好。基于深度网络的one-stage算法实时性好，但是经常会把变化的背景、运动的非车辆物体纳入其中，同时对光照变化敏感，夜间分类效果不好。因此，提出采用one-stage做目标检测，并不直接获取分类结果，而是根据标注框将物体切割出来，去除背景，提升抗背景扰动性能和分类效果；再送入一个经过迁移学习的浅层神经网络；将分类输出和目标检测网络的位置输出合并送入一个全图匹配算法，进行车流量统计。该方法在保障实时性的同时降低了漏检和复检率。
中圖分類號(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)車輛檢測(cè)分類與車流量統(tǒng)計(jì)算法［J］.信息技術(shù)與網(wǎng)絡(luò)安全，2020,39（3）：6267,72.
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)車輛檢測(cè)分類與車流量統(tǒng)計(jì)算法［J］.信息技術(shù)與網(wǎng)絡(luò)安全，2020,39（3）：6267,72.
Highway video real-time vehicle detection classification and traffic flow statistics algorithm
Zha Weiwei, Bai Tian
(Department of Software Engineering,University of Science and Technology of China,Hefei 230000,China)
Abstract： Realtime 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 reinspection,so the automation performance is not good.The realtime performance of onestage algorithm based on deep network can be guaranteed,but the changing background,moving nonvehicle 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 onestage,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 realtime performance,the rate of missed inspections and reinspections is reduced.
Key words : convolutional neural network;target detection and classification;realtime traffic statistics;YOLOv3 network
0 引言
公路視頻的車輛分類與車流量統(tǒng)計(jì)是運(yùn)動(dòng)物體目標(biāo)檢測(cè)識(shí)別與跟蹤問(wèn)題，可以通過(guò)傳統(tǒng)圖像方法和現(xiàn)代深度網(wǎng)絡(luò)實(shí)現(xiàn)。傳統(tǒng)圖像方法由于計(jì)算量較小，因此實(shí)時(shí)性相對(duì)較高?，F(xiàn)代深度網(wǎng)絡(luò)在背景分割、目標(biāo)分類的準(zhǔn)確度上有著壓倒性的優(yōu)勢(shì)。
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作者信息：
查偉偉,白天
(中國(guó)科學(xué)技術(shù)大學(xué) 軟件學(xué)院,安徽 合肥 230000)
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