A deep learning approach of vehicle multitarget detection from traffic video

2Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

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.

Cite

CITATION STYLE

APA

Li, X., Liu, Y., Zhao, Z., Zhang, Y., & He, L. (2018). A deep learning approach of vehicle multitarget detection from traffic video. Journal of Advanced Transportation, 2018. https://doi.org/10.1155/2018/7075814

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free