Volume 22 Issue 3
Jun.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 225-237
CHEN Ting, YAO Da-chun, GAO Tao, QIU Hui-hui, GUO Chang-xin, LIU Zhan-wen, LI Yong-hui, BIAN Hao-yi. A fused network based on PReNet and YOLOv4 for traffic object detection in rainy environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 225-237. doi: 10.19818/j.cnki.1671-1637.2022.03.018
Citation: CHEN Ting, YAO Da-chun, GAO Tao, QIU Hui-hui, GUO Chang-xin, LIU Zhan-wen, LI Yong-hui, BIAN Hao-yi. A fused network based on PReNet and YOLOv4 for traffic object detection in rainy environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 225-237. doi: 10.19818/j.cnki.1671-1637.2022.03.018
shu

A fused network based on PReNet and YOLOv4 for traffic object detection in rainy environment

doi: 10.19818/j.cnki.1671-1637.2022.03.018
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Branch of Shaanxi, Bank of Communications Co., Ltd., Xi'an 710004, Shaanxi, China

  • 3.

    School of Electrical and Information Engineering, The University of Sydney, Sydney NSW2006, New South Wales, Australia

  • 4.

    Zhejiang Institute of Mechanical and Electrical Technology, Hangzhou 310053, Zhejiang, China

Funds:

National Key Research and Development Program of China 2019YFE0108300

National Key Research and Development Program of China 2018YFB1600600

National Natural Science Foundation of China 62001058

National Natural Science Foundation of China 52172379

Fundamental Research Funds for the Central Universities 300102241201

Fundamental Research Funds for the Central Universities 300102242901

Soft Science Project of Science and Technology Department of Zhejiang Province 2021C25005

Science and Technology Project of Department of Transportation of Zhejiang Province 2021032

More Information
  • Author Bio:

    CHEN Ting (1982-), female, associate professor, PhD, tchenchd@126.com

    GAO Tao(1980-), male, professor, PhD, gtnwpu@126.com

  • Received Date: 2022-03-22
  • Publish Date: 2022-06-25
    Abstract
  • In order to improve the detection accuracy of vehicle target in severe rainy day under traffic environment, a deep learning network DTOD-PReYOLOv4 (derain and traffic object detection-PReNet and YOLOv4) was proposed based on the fusion of PReNet and YOLOv4, which integrated the improved image restoration subnet D-PReNet and the improved target detection subnet TOD-YOLOv4. D-PReNet could extract rain streak features more effectively, since it introduced the multi-scale expansion convolution fusion module (MSECFM) and the attentional mechanism residual module (AMRM) with SEBlock into PReNet. TOD-YOLOv4 improved not only the detection accuracy of small traffic target, but also the detection efficiency, since it replaced the backbone module CSPDarknet53 of YOLOv4 with the lightweight CSPDarknet26 of YOLOv4, added CRB into PANet of YOLOv4 neck, and utilized k-means++ instead of the original network clustering algorithm. DTOD-PReYOLOv4 was verified based on the constructed vehicle target database VOD-RTE in rainy day traffic scenario. Research results show that compared with the current series of YOLO networks, the proposed DTOD-PReYOLOv4 can better extract the features with lower resolutions by superimposing RB over ResBlock_body1 in the shallow layer. It can effectively reduce the convolutional layer redundancy and improve the memory utilization, since ResBlock_body3, ResBlock_body4 and ResBlock_body5 in deep layer can be properly cropped to ResBlock_body3×2, ResBlock_body4×2 and ResBlock_body5×2, respectively. It also can alleviate the degradation of small target detection effect caused by the deepening of network layers by adding jump connection to Concat+Conv×5 in PANet to form CRB. In the process of multi-scale detection, k-means++ algorithm is adopted to allocate smaller prior boxes that are more suitable for the larger feature images, but larger prior boxes that are more suitable for smaller feature images, which further improves the accuracy of target detection. The harmonic mean value of precision and recall rate, average precision and detection speed of DTOD-PReYOLOv4 respectively increase by 5.02%, 6.70% and 15.63 frames per second compared with MYOLOv4, by 3.51%, 4.31% and 2.17 frames per second compared with TOD-YOLOv4, by 46.07%, 48.05% and 18.97 frames per second compared with YOLOv3, and by 31.06%, 29.74% and 16.26 frames per second compared with YOLOv4. 4 tabs, 12 figs, 44 refs.

     

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