sis 18: e31

Research Article

A novel one-stage object detection network for multi-scene vehicle attribute recognition

Download36 downloads
  • @ARTICLE{10.4108/eai.22-11-2021.172217,
        author={Jiefei Zhang},
        title={A novel one-stage object detection network for multi-scene vehicle attribute recognition},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={11},
        keywords={multi-scene vehicle attribute recognition, YOLOv3, GIOU loss function},
        doi={10.4108/eai.22-11-2021.172217}
    }
    
  • Jiefei Zhang
    Year: 2021
    A novel one-stage object detection network for multi-scene vehicle attribute recognition
    SIS
    EAI
    DOI: 10.4108/eai.22-11-2021.172217
Jiefei Zhang1,*
  • 1: School of Automobiles, Henan College of Transportation, Zhengzhou 450000, China
*Contact email: aqiufenga@163.com

Abstract

In recent years, with the continuous development of computer vision technology, computer vision has been widely used in many scientific research fields and civil applications. As one of the basic tasks of many advanced visual tasks, object detection has important research significance in the field of computer vision and practical applications. At present, with the joint efforts of many scholars, the research on object detection based on deep learning has made remarkable progress. However, in some special weather, such as rainy days, foggy days, nights and the lack of visible light source, the visual distance and visibility are very poor, and the obtained images cannot be used normally, thus affecting the result of object detection. To solve the above problem, this paper proposes a novel one-stage object detection network for multi-scene vehicle attribute recognition, which mainly contains vehicle type and color attributes. The one-stage object detection network YOLOv3 is used as the basic network, and GIOU loss function is used to replace MSE loss function. Finally, experimental results show that the accuracy of the proposed algorithm is improved significantly on public data sets.