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Laser & Optoelectronics Progress, Volume. 62, Issue 10, 1037009(2025)

Unmanned Aerial Vehicle Small Target Detection Algorithm Based on YOLOv8n

Lizhi Zhu1 and Hui Wei1,2、*
Author Affiliations
  • 1School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, Anhui , China
  • 2School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, Guangxi , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
    Equations (6)
    References (32)
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    Figures & Tables(11)
    YOLOv8 network architecture

    Fig. 1. YOLOv8 network architecture

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    Improved algorithm architecture based on YOLOv8n network

    Fig. 2. Improved algorithm architecture based on YOLOv8n network

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    TFE feature concatenation module

    Fig. 3. TFE feature concatenation module

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    Structure of SSFF module

    Fig. 4. Structure of SSFF module

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    Distribution of target sizes

    Fig. 5. Distribution of target sizes

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    MSFH structure

    Fig. 6. MSFH structure

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    Visual comparison of detection results of YOLOv8n and improved algorithm in different scenarios. (a) Parking and road scene; (b) square scene

    Fig. 7. Visual comparison of detection results of YOLOv8n and improved algorithm in different scenarios. (a) Parking and road scene; (b) square scene

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    Visual comparison of Grad-CAM for YOLOv8n and improved algorithm. (a) Parking and road scene; (b) square scene

    Fig. 8. Visual comparison of Grad-CAM for YOLOv8n and improved algorithm. (a) Parking and road scene; (b) square scene

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    • Table 1. Comparative experiment results of different algorithms

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      Table 1. Comparative experiment results of different algorithms

      Algorithm

      mAP50 /

      %

      mAP50-95 /

      %

      Parameters /

      106

      		FLOPS /109
      YOLOv5s26.313.57.0415.8
      YOLOv7-tiny29.714.86.0313.1
      YOLOv8n26.414.73.018.1
      YOLOv8s32.018.211.1028.5
      YOLOv10n26.814.62.276.5
      YOLOv11n26.514.72.586.3
      DAMO-YOLO21.111.68.2017.2
      Gold-YOLO24.413.65.6112.1
      Ours30.216.51.389.0

    • Table 2. mAP50 comparison for ten targets

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      Table 2. mAP50 comparison for ten targets

      AlgorithmAwn-trBicycleBusCarMotorPedestrianPeopleTricycleTruckVan
      YOLOv5s12.27.349.967.722.424.215.711.024.828.2
      YOLOv7-tiny15.68.851.070.527.725.819.414.730.932.7
      YOLOv8n13.46.148.965.923.122.811.511.430.930.4
      YOLOv11n13.85.750.166.223.021.811.812.830.829.4
      Ours15.89.251.072.028.228.917.815.029.434.9

    • Table 3. Ablation experiment results

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      Table 3. Ablation experiment results

      SSFFTFEMSFHConvNext v2LAMPmAP50 /%mAP50-95 /%Parameters /106FLOPS /109Time /ms
      26.414.73.018.110.5
      √27.015.03.048.311.5
      √√27.715.63.058.512.1
      √√√29.416.32.4912.014.8
      √√√√32.217.65.1618.215.9
      √√√√√30.216.51.389.015.5
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    Lizhi Zhu, Hui Wei. Unmanned Aerial Vehicle Small Target Detection Algorithm Based on YOLOv8n[J]. Laser & Optoelectronics Progress, 2025, 62(10): 1037009

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    Paper Information

    Category: Digital Image Processing

    Received: Sep. 24, 2024

    Accepted: Dec. 2, 2024

    Published Online: Apr. 28, 2025

    The Author Email: Hui Wei (89192138@qq.com)

    DOI:10.3788/LOP242022

    CSTR:32186.14.LOP242022

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology