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A Vehicle Detection Method Based on an Improved U-YOLO Network for High-Resolution Remote-Sensing Images

Author

Listed:
  • Dudu Guo

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430070, China
    College of Transportation Engineering, Xinjiang University, Urumqi 830046, China)

  • Yang Wang

    (College of Transportation Engineering, Xinjiang University, Urumqi 830046, China)

  • Shunying Zhu

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Xin Li

    (College of Transportation Engineering, Xinjiang University, Urumqi 830046, China)

Abstract

The lack of vehicle feature information and the limited number of pixels in high-definition remote-sensing images causes difficulties in vehicle detection. This paper proposes U-YOLO, a vehicle detection method that integrates multi-scale features, attention mechanisms, and sub-pixel convolution. The adaptive fusion module (AF) is added to the backbone of the YOLO detection model to increase the underlying structural information of the feature map. Cross-scale channel attention (CSCA) is introduced to the feature fusion part to obtain the vehicle’s explicit semantic information and further refine the feature map. The sub-pixel convolution module (SC) is used to replace the linear interpolation up-sampling of the original model, and the vehicle target feature map is enlarged to further improve the vehicle detection accuracy. The detection accuracies on the open-source datasets NWPU VHR-10 and DOTA were 91.35% and 71.38%. Compared with the original network model, the detection accuracy on these two datasets was increased by 6.89% and 4.94%, respectively. Compared with the classic target detection networks commonly used in RFBnet, M2det, and SSD300, the average accuracy rate values increased by 6.84%, 6.38%, and 12.41%, respectively. The proposed method effectively solves the problem of low vehicle detection accuracy. It provides an effective basis for promoting the application of high-definition remote-sensing images in traffic target detection and traffic flow parameter detection.

Suggested Citation

  • Dudu Guo & Yang Wang & Shunying Zhu & Xin Li, 2023. "A Vehicle Detection Method Based on an Improved U-YOLO Network for High-Resolution Remote-Sensing Images," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10397-:d:1184736
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    References listed on IDEAS

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    1. Zhihua Hua & Haiyang Yu & Peng Jing & Caoyuan Song & Saifei Xie, 2023. "A Light-Weight Neural Network Using Multiscale Hybrid Attention for Building Change Detection," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
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    Cited by:

    1. Roman Ekhlakov & Nikita Andriyanov, 2024. "Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision," Mathematics, MDPI, vol. 12(4), pages 1-27, February.

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