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YOLO series algorithms in object detection of unmanned aerial vehicles: a survey

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Abstract

YOLO series algorithms are widely used in unmanned aerial vehicles (UAV) object detection scenarios due to their fast and lightweight properties. This article summarizes the key concepts in YOLO series algorithms, such as the anchor mechanism, feature fusion strategy, bounding box regression loss and so on and points out the advantages and improvement space of the YOLO series algorithms. Discussing the relevant technologies of the YOLOv1 to YOLOv7 series algorithms in detail in three parts: basic structure, strengths and weaknesses, and compares the algorithm performance. On this basis, combined with the challenges of object detection technology in UAV applications, various solutions for improving the YOLO series algorithms and applying them to UAV object detection scenarios are demonstrated. The improvement strategies, application scenarios, academic contributions and limitations of the algorithms are summarized. Finally, the future development directions and challenges of applying YOLO series algorithms to UAV object recognition are prospected.

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Abbreviations

AP:

Average precision

BN:

Batch normalization

CBAM:

Convolutional block attention module

CBL:

Convolutions + BN + Leaky ReLU activation function

CBM:

Convolution + BN + mish activation function

CIoU:

Complete IoU

CNN:

Convolutional neural networks

CSP:

Cross stage partial

DIoU:

Distance IoU

FLOPS:

Floating point operations per second

FPN:

Feature pyramid network

FPS:

Frame per second

GIoU:

Generalized IoU

IoU:

Intersection over union

mAP:

Mean average precision

MSE:

Mean square error

NMS:

Non-maximum suppression

OTA:

Optimal transport assignment

PANet:

Path aggregation network

R-CNN:

Region with CNN feature

ResNet:

Residual network

RPN:

Region proposal network

SAM:

Spatial attention module

SIoU:

Soft IoU

SPP:

Spatial pyramid pooling

SSD:

Single shot MultiBox detector

TAL:

Task alignment learning

TAP:

Task-aligned predictors

TOOD:

Task-aligned one-stage object detection

VGGNet:

Visual geometry group network

YOLO:

You only look once

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Acknowledgements

The authors extend their sincere appreciation to the Faculty of Information Sciences and Engineering and the Software Engineering and Digital Innovation Center at Management and Science University, Malaysia, for their invaluable assistance and support during the course of this study. The authors gratefully acknowledge the financial supports by the Sichuan Provincial Intellectual Property Special Fund Project under project number 2022-ZS-00156.

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Authors and Affiliations

  1. School of Graduates Studies of Management and Science University, Shah Alam, Selangor, Malaysia

    Li Jiao

  2. Mianyang Polytechnic, Mianyang, Sichuan, China

    Li Jiao

  3. Software Engineering and Digital Innovation Center, Management and Science University, Shah Alam, Selangor, Malaysia

    Muhammad Irsyad Abdullah

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  1. Li Jiao
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Correspondence to Muhammad Irsyad Abdullah.

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Jiao, L., Abdullah, M.I. YOLO series algorithms in object detection of unmanned aerial vehicles: a survey. SOCA (2024). https://doi.org/10.1007/s11761-024-00388-w

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