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Light-weighted vehicle detection network based on improved YOLOv3-tiny

Author

Listed:
  • Pingshu Ge
  • Lie Guo
  • Danni He
  • Liang Huang

Abstract

Vehicle detection is one of the most challenging research works on environment perception for intelligent vehicle. The commonly used object detection network is too large and can only be realized in real-time on a high-performance server. Based on YOLOv3-tiny, the feature extraction was realized using light-weighted networks such as DarkNet-19 and ResNet-18 to improve accuracy. The K -means algorithm was used to cluster nine anchor boxes to achieve multi-scale prediction, especially for small targets. For automotive applicable scenarios, the proposed vehicle detection network was executed in an embedded device. The KITTI data sets were trained and tested. Experimental results show that the average accuracy is improved by 14.09% compared with the traditional YOLOv3-tiny, reaching 93.66%, and can reach 13 fps on the embedded device.

Suggested Citation

  • Pingshu Ge & Lie Guo & Danni He & Liang Huang, 2022. "Light-weighted vehicle detection network based on improved YOLOv3-tiny," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501329221, March.
    • Handle: RePEc:sae:intdis:v:18:y:2022:i:3:p:15501329221080665
    DOI: 10.1177/15501329221080665
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    References listed on IDEAS

    as
    1. Hoanh Nguyen & Kai Hu, 2021. "Multiscale Feature Learning Based on Enhanced Feature Pyramid for Vehicle Detection," Complexity, Hindawi, vol. 2021, pages 1-10, June.
    2. Ijaz Ul Haq & Khan Muhammad & Tanveer Hussain & Soonil Kwon & Maleerat Sodanil & Sung Wook Baik & Mi Young Lee, 2019. "Movie scene segmentation using object detection and set theory," International Journal of Distributed Sensor Networks, , vol. 15(6), pages 15501477198, June.
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