A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video
Joint Authors
Li, Xun
Zhao, Zhengfan
Liu, Yao
Zhang, Yue
He, Li
Source
Journal of Advanced Transportation
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Vehicle detection is expected to be robust and efficient in various scenes.
We propose a multivehicle detection method, which consists of YOLO under the Darknet framework.
We also improve the YOLO-voc structure according to the change of the target scene and traffic flow.
The classification training model is obtained based on ImageNet and the parameters are fine-tuned according to the training results and the vehicle characteristics.
Finally, we obtain an effective YOLO-vocRV network for road vehicles detection.
In order to verify the performance of our method, the experiment is carried out on different vehicle flow states and compared with the classical YOLO-voc, YOLO 9000, and YOLO v3.
The experimental results show that our method achieves the detection rate of 98.6% in free flow state, 97.8% in synchronous flow state, and 96.3% in blocking flow state, respectively.
In addition, our proposed method has less false detection rate than previous works and shows good robustness.
American Psychological Association (APA)
Li, Xun& Liu, Yao& Zhao, Zhengfan& Zhang, Yue& He, Li. 2018. A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1181612
Modern Language Association (MLA)
Li, Xun…[et al.]. A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video. Journal of Advanced Transportation No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1181612
American Medical Association (AMA)
Li, Xun& Liu, Yao& Zhao, Zhengfan& Zhang, Yue& He, Li. A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1181612
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181612