Unauthorized unmanned aerial vehicles (UAVs) pose threats to public safety and individual privacy. Traditional object-detection approaches often fall short during their application in anti-UAV technologies. To address this issue, we propose the YOLOv7-GS model, which is designed specifically for the identification of small UAVs in complex and low-altitude environments. This research primarily aims to improve the model’s detection capabilities for small UAVs in complex backgrounds. Enhancements were applied to the YOLOv7-tiny model, including adjustments to the sizes of prior boxes, incorporation of the InceptionNeXt module at the end of the neck section, and introduction of the SPPFCSPC-SR and Get-and-Send modules. These modifications aid in the preservation of details about small UAVs and heighten the model’s focus on them. The YOLOv7-GS model achieves commendable results on the DUT Anti-UAV and the Amateur Unmanned Air Vehicle Detection datasets and performs to be competitive against other mainstream algorithms.


    Access

    Download


    Export, share and cite



    Title :

    Vision-Based Anti-UAV Detection Based on YOLOv7-GS in Complex Backgrounds


    Contributors:
    Chunjuan Bo (author) / Yuntao Wei (author) / Xiujia Wang (author) / Zhan Shi (author) / Ying Xiao (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Vision-based UAV Detection in Complex Backgrounds and Rainy Conditions

    Singh, Pranita / Gupta, Keshav / Jain, Amit Kumar et al. | IEEE | 2024


    Road pothole detection based on improved YOLOv7

    Zhang, Jianli / Lei, Jiaofei | SPIE | 2023



    UAV-based Real-Time Face Detection using YOLOv7

    Samma, Hussein / Al-Azani, Sadam / El-Ferik, Sami | Elsevier | 2025

    Free access

    YOLOv7-SPD3: A Small Target Detection Algorithm for Multi-Rotor UAV Based on Improved YOLOv7

    He, Xin / Fan, Kuangang / Zhang, Xuetao et al. | Springer Verlag | 2025