چکیده مقاله
Human traffic monitoring can be a difficult and wearisome task Because the human operator in trafficmanagement center deals with the videos of a large number of cameras installed on the network The use of automation canhelp reduce the workload of human operators and facilitate traffic management that reduces the frequent accidents andcongestion on the roads In this paper, we have proposed an automatic traffic monitoring system that uses two deep learningalgorithms YOLO and Mask RCN for traffic management We used a large database of traffic camera images to train deeplearning based models for congestion detection, traffic nodes detection, and vehicle count detection For traffic monitoring, wehave presented a Mask R CNN neural network In this method, the traffic node is predicted using pixel level segmentationmasks in classified areas Also, this model is used to accurately extract information about traffic queues with video camerasinstalled on the street Thus, initially, a YOLO convolutional neural network model, is used for real time vehicle detection andclassification The identification model is then used with a multi object tracking system based on IOU to search and examinevarious traffic scenes for possible anomalies The obtained results show that the proposed framework performs satisfactorily indifferent conditions without being affected by environmental hazards such as camera blur, low light, rain or snow
کلیدواژهها
نویسندگان
Mona Kakaie
departments of computer scienceLian universityBushehr, Iran
Marzieh Dadvar
departments of computer scienceLian universityBushehr, Iran
Hasan Arfaei Nia
departments of computer scienceLian universityBushehr, Iran
شیوه ارجاع
Kakaie, Mona and Dadvar, Marzieh and Arfaei Nia, Hasan,1402,Traffic management using two deep neural networks YOLO and Mask R-CNN,19th National Conference of Computer Science and Engineering and Information Technology,Babol
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