A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/7075814 |
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| _version_ | 1849307848127807488 |
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| author | Xun Li Yao Liu Zhengfan Zhao Yue Zhang Li He |
| author_facet | Xun Li Yao Liu Zhengfan Zhao Yue Zhang Li He |
| author_sort | Xun Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2a4b50e7f5044f72a3fb6435097055f5 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-2a4b50e7f5044f72a3fb6435097055f52025-08-20T03:54:37ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/70758147075814A Deep Learning Approach of Vehicle Multitarget Detection from Traffic VideoXun Li0Yao Liu1Zhengfan Zhao2Yue Zhang3Li He4School of Electronic Information, Xi'an Polytechnical University, Xi’an 710048, ChinaSchool of Electronic Information, Xi'an Polytechnical University, Xi’an 710048, ChinaBeijing Special Electromechanical Technology Research Institute, Beijing 10012, ChinaSchool of Electronic Information, Xi'an Polytechnical University, Xi’an 710048, ChinaGuangdong University of Technology, Guangzhou 510006, ChinaVehicle 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.http://dx.doi.org/10.1155/2018/7075814 |
| spellingShingle | Xun Li Yao Liu Zhengfan Zhao Yue Zhang Li He A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video Journal of Advanced Transportation |
| title | A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video |
| title_full | A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video |
| title_fullStr | A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video |
| title_full_unstemmed | A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video |
| title_short | A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video |
| title_sort | deep learning approach of vehicle multitarget detection from traffic video |
| url | http://dx.doi.org/10.1155/2018/7075814 |
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