基于YOLOv10的无人机复杂背景下多尺度检测模型

黄毅; 周纯; 刘欣军; 陈庆

doi:10.13756/j.gtxyj.2025.240158

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基于YOLOv10的无人机复杂背景下多尺度检测模型

光通信系统与网络技术 | 更新时间：2025-10-15

- 基于YOLOv10的无人机复杂背景下多尺度检测模型
- Multi-Scale Detection Model for Complex Backgrounds in UAV Images based on YOLOv10
- 光通信研究 2025年第5期
- 作者机构：
  
  1.广州现代信息工程职业技术学院　信息工程学院，广州　510000
  2.上海华讯网络系统有限公司，上海　201103
  3.广西大学　计算机与电子信息学院，南宁　530004
- 作者简介：
  
  黄毅（1988-），男，湖北随州人。高级工程师，硕士，主要研究方向为网络安全、图像处理和目标检测。
  陈庆，高级工程师。E-mail：chenging_aaud_edu@163.com
- 基金信息：
  
  国家自然科学基金资助项目(62003104)
- DOI：10.13756/j.gtxyj.2025.240158
  中图分类号： TN911.73-34;TP391.41
- 收稿：2024-07-23，
  
  修回：2024-08-14，
  
  纸质出版：2025-10-10
- 稿件说明：
移动端阅览
黄毅，周纯，刘欣军，等. 基于YOLOv10的无人机复杂背景下多尺度检测模型[J]. 光通信研究，2025(5): 240158.

Huang Y, Zhou C, Liu X J, et al. Multi-Scale Detection Model for Complex Backgrounds in UAV Images based on YOLOv10[J]. Study on Optical Communications, 2025(5): 240158.
黄毅，周纯，刘欣军，等. 基于YOLOv10的无人机复杂背景下多尺度检测模型[J]. 光通信研究，2025(5): 240158. DOI： 10.13756/j.gtxyj.2025.240158.

Huang Y, Zhou C, Liu X J, et al. Multi-Scale Detection Model for Complex Backgrounds in UAV Images based on YOLOv10[J]. Study on Optical Communications, 2025(5): 240158. DOI： 10.13756/j.gtxyj.2025.240158.

摘要

【目的】

针对无人机（UAV）航拍图像中复杂背景下多尺度目标检测挑战，文章提出了一种基于YOLOv10模型改进的轻量化检测模型，以提升UAV在复杂背景下多尺度目标的检测性能。

【方法】

在YOLOv10模型的基础上，文章首先引入了多通道逆残差模块（MCIR），该模块通过利用多通道处理策略与逆残差（IR）结构，提升了模型在复杂背景中对多尺度目标的特征提取和融合能力；其次，文章在跨阶段部分（CSP）双卷积瓶颈层（C2f）基础上引入了卷积块注意力模块（CBAM）注意力机制得到C2f-CM，增强了复杂背景中对特征目标的定位能力；随后，文章基于MCIR和C2f-CM对YOLOv10模型的骨干网络（Backbone）和颈部网络（Neck）进行了轻量化处理，主要是在Backbone中减少下采样次数以保留更多的特征信息，在Neck中通过优化上采样和特征拼接操作，减少网络层数和复杂度，进一步降低计算开销；最后，文章将原损失函数优化为Focal-EIOU，能够更精准地匹配预测框和真实框，并有效解决数据集中样本不平衡问题。

【结果】

实验结果表明，改进后的轻量化目标检测（LED）-YOLOv10模型相较于原始YOLOv10模型，在mAP50（平均精度）上提升了9.8%，达到了44.5%；参数量和模型大小分别减少了65.68%和51.60%，降至0.927 MB和2.700 MB。消融实验进一步验证了改进模块MCIR、C2f-CM和Focal-EIOU的有效性，显示其在提升检测精度的同时，显著降低了模型的复杂度。对比实验结果表明，LED-YOLOv10在复杂背景下的多尺度场景中综合表现最优，检测精度和模型大小均优于其他目标检测算法，检测速度也能够满足实时检测的需求。在嵌入式设备Jetson Nano中对该轻量级模型的综合性能评估也进一步表明，LED-YOLOv10更适合在资源受限的嵌入式平台部署使用。

【结论】

文章提出的LED-YOLOv10模型，显著提升了UAV航拍图像中复杂背景下多尺度目标检测的精度，并大幅度减少了模型参数量和大小。实验结果和检测效果也验证了文章所提方法在复杂背景下多尺度目标检测中的优越性，为UAV目标检测提供了一种高效、轻量的解决方案。

Abstract

【Objective】

To address the challenges of multi-scale object detection in complex backgrounds of Unmanned Aerial Vehicle (UAV) aerial images

this paper proposes a lightweight detection model based on an improved YOLOv10 to enhance the detection performance of Unmanned Aerial Vehicle (UAV) s in complex backgrounds.

【Methods】

Based on YOLOv10

the Multi-channel Inverted Residual Block (MCIR) was introduced first. This module uses a multichannel processing strategy and an Inverted Residual (IR) structure to enhance the model's feature extraction and fusion capabilities for multi-scale objects in complex backgrounds. Second

the Convolutional Block Attention Module (CBAM) was introduced into the Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f) module to create the C2f-CM

which improves the localization of feature objects in complex backgrounds. Then

the backbone and neck networks of YOLOv10 were lightweighted based on MCIR and C2f-CM. This was achieved by reducing the number of downsampling operations in the backbone network to retain more feature information

and by optimizing the upsampling and feature concatenation operations in the neck network to reduce the number of layers and complexity

thereby further reducing the computational overhead. Finally

the original loss function was optimized to Focal-EIoU

which can more accurately match the predicted and real boxes and effectively address the issue of sample imbalance in the dataset.

【Results】

Experimental results show that the improved Lightweight Enhanced Detection (LED) -YOLOv10 model achieved a 9.8 percentage point increase in mAP50 (average precision) compared to the original YOLOv10

reaching 44.5%. The parameter count and model size were reduced by 65.68% and 51.60%

respectively

to 0.927 and 2.700 MB. Ablation experiments further validated the effectiveness of the improved MCIR

C2f-CM

and Focal-EIoU modules

demonstrating significant improvements in detection accuracy while substantially reducing model complexity. Comparative experiments show that LED-YOLOv10 performs optimally in multi-scale scenarios with complex backgrounds

with superior detection accuracy and model size compared to other object detection algorithms. Its detection speed can meet the requirements for real-time detection. The comprehensive performance evaluation of this lightweight model on the embedded device Jetson Nano further indicates that LED-YOLOv10 is more suitable for deployment on resource-constrained embedded platforms.

【Conclusion】

The proposed LED-YOLOv10 model significantly improves the accuracy of multi-scale object detection in complex backgrounds of UAV aerial images while greatly reducing the model's parameter count and size. The experimental results and detection performance validate the superiority of the proposed method in multi-scale object detection in complex backgrounds

providing an efficient and lightweight solution for UAV object detection.

关键词

Keywords

references

张明淳 , 牛春晖 , 刘力双 , 等 . 用于无人机探测系统的红外小目标检测算法 [J ] . 激光技术 , 2024 , 48 ( 1 ): 114 - 120 .

Zhang M C , Niu C H , Liu L S , et al . Infrared Small Target Detection Algorithm for UAV Detection System [J ] . Laser Technology , 2024 , 48 ( 1 ): 114 - 120 .

安宁 . 全光通信网络非线性突变频率干扰检测算法 [J ] . 光通信研究 , 2024 ( 3 ): 230064 .

An N . Nonlinear Mutation Frequency Interference Detection Algorithm for All Optical Communication Networks [J ] . Study on Optical Communications , 2024 ( 3 ): 230064 .

姚楚羡 , 蔡皓楠 , 张远波 , 等 . 基于轻量化车载设备的道路病害检测方法 [J ] . 测绘通报 , 2024 ( 5 ): 147 - 150 .

Yao C X , Cai H N , Zhang Y B , et al . Road Damage Detection Method based on Lightweight Vehicle Equipment [J ] . Bulletin of Surveying and Mapping , 2024 ( 5 ): 147 - 150 .

冷佳旭 , 莫梦竟成 , 周应华 , 等 . 无人机视角下的目标检测研究进展 [J ] . 中国图象图形学报 , 2023 , 28 ( 9 ): 2563 - 2586 .

Leng J X , Mo M J C , Zhou Y H , et al . Recent Advances in Drone-view Object Detection [J ] . Journal of Image and Graphics , 2023 , 28 ( 9 ): 2563 - 2586 .

赵扬 , 杨平华 , 王铭振 , 等 . 钛合金增材制造孔隙缺陷的无损检测研究进展 [J ] . 激光技术 , 2024 , 48 ( 3 ): 432 - 437 .

Zhao Y , Yang P H , Wang M Z , et al . Progress in Nondestructive Testing of Pore Defects in Titanium Alloy Additive Manufacturing [J ] . Laser Technology , 2024 , 48 ( 3 ): 432 - 437 .

孙日明 , 郭虎 , 邹丽 , 等 . 融合决策下的数字序列跌倒检测方法 [J ] . 系统仿真学报 , 2023 , 35 ( 9 ): 2045 - 2053 .

Sun R M , Guo H , Zou L , et al . Fall Detection Method of Digital Sequence based on Fusion Strategy [J ] . Journal of System Simulation , 2023 , 35 ( 9 ): 2045 - 2053 .

郭庆梅 , 刘宁波 , 王中训 , 等 . 基于深度学习的目标检测算法综述 [J ] . 探测与控制学报 , 2023 , 45 ( 6 ): 10 - 20 .

Guo Q M , Liu N B , Wang Z X , et al . Review of Deep Learning based Object Detection Algorithms [J ] . Journal of Detection & Control , 2023 , 45 ( 6 ): 10 - 20 .

张润梅 , 肖钰霏 , 贾振楠 , 等 . 改进YOLOv7的无人机视角下复杂环境目标检测算法 [J ] . 光电工程 , 2024 , 51 ( 5 ): 92 - 103 .

Zhang R M , Xiao Y F , Jia Z N , et al . Improved YO-LOv7 Algorithm for Target Detection in Complex Environments from UAV Perspective [J ] . Opto-Electronic Engineering , 2024 , 51 ( 5 ): 92 - 103 .

郝紫霄 , 王琦 . 基于YOLO-v7的无人机航拍图像小目标检测改进算法 [J ] . 软件导刊 , 2024 , 23 ( 1 ): 167 - 172 .

Hao Z X , Wang Q . Enhanced Algorithm for Small Target Detection in UAV Aerial Images based on YOLO-v7 [J ] . Software Guide , 2024 , 23 ( 1 ): 167 - 172 .

胡峻峰 , 李柏聪 , 朱昊 , 等 . 改进YOLOv8的轻量化无人机目标检测算法 [J ] . 计算机工程与应用 , 2024 , 60 ( 8 ): 182 - 191 .

Hu J F , Li B C , Zhu H , et al . Improved YOLOv8 Lightweight UAV Target Detection Algorithm [J ] . Computer Engineering and Applications , 2024 , 60 ( 8 ): 182 - 191 .

Woo S , Park J , Lee J Y , et al . CBAM: Convolutional Block Attention Module [C ] // Computer Vision-ECCV 2018: 15th European Conference . Munich, Germany : Springer , 2018 : 3 - 19 .

Sandler M , Howard A , Zhu M , et al . MobileNetV2: Inverted Residuals and Linear Bottlenecks [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City, UT, USA : IEEE , 2018 : 8578572 .

Yang Z , Wang X , Li J . EIoU: an Improved Vehicle Detection Algorithm based on VehicleNet Neural Network [J ] . Journal of Physics: Conference Series , 2021 , 1924 ( 1 ): 012001 .

Zhang Y F , Ren W , Zhang Z , et al . Focal and Efficient IOU Loss for Accurate Bounding Box Regression [J ] . Neurocomputing , 2022 , 506 : 146 - 157 .

Du D , Wen L , Zhu P , et al . VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results [C ] // Computer Vision-ECCV 2020 Workshops . Cham, Switzerland : Springer International Publishing , 2020 : 692 - 712 .

Liu W , Anguelov D , Erhan D , et al . SSD: Single Shot MultiBox Detector [C ] // Computer Vision-ECCV 2016 . Cham, Switzerland : Springer International Publishing , 2016 : 21 - 37 .

Girshick R . Fast R-CNN [C ] // 2015 IEEE International Conference on Computer Vision (ICCV) . Santiago, Chile : IEEE , 2015 : 7410526 .

Yang Y , Gao X , Wang Y , et al . VAMYOLOX: an Accurate and Efficient Object Detection Algorithm based on Visual Attention Mechanism for UAV Optical Sensors [J ] . IEEE Sensors Journal , 2023 , 23 ( 11 ): 11139 - 11155 .

Yang R , Li W , Shang X , et al . KPE-YOLOv5: an Improved Small Target Detection Algorithm based on YOLOv5 [J ] . Electronics , 2023 , 12 ( 4 ): 817 .

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