Air-to-Ground Dense Small Target Detection Algorithm Based on Multi-scale Feature Fusion

doi:10.19678/j.issn.1000-3428.0252144

Abstract

Abstract: Unmanned Aerial Vehicle (UAV) low-altitude aerial photography technology has been widely used in various fields, such as data acquisition, security monitoring, and terrain mapping, and has significantly improved operational efficiency. However, because aerial images typically cover a wide area, the detection targets are often small, dense, and unevenly distributed, limiting the accuracy of target detection. To address these issues, a target detection algorithm, TOD-YOLO, is proposed based on an improved YOLOv8 network. First, an RFAConv downsampling module is introduced into the backbone network, which provides effective attention to the convolutional kernel without increasing the number of parameters, thereby allowing the network to focus on smaller targets in the feature extraction phase. Second, the feature fusion module CSPOKM and a new feature fusion path are proposed to increase the focus on small targets and improve the performance of the feature fusion network. Subsequently, based on the depthwise separable convolution DWConv and the Efficient Multi-scale Attention (EMA) mechanism, the lightweight attention detection head DWA-Head is designed to reduce the number of parameters while improving the detection accuracy. Finally, to solve the problem of Complete Intersection over Union (CIoU) being easy to misdetect and remain undetected in complex scenes with a high density of small targets, DA-MPDIoU, a loss function that can dynamically adjust the positive and negative sample coefficients, is designed to assign higher weights to small target samples that are easy to miss and difficult to classify and optimize the training results. The experimental results show that, compared with the YOLOv8n algorithm, the proposed algorithm improves mAP@0.5 and mAP@0.5:0.95 on the VisDrone2019 dataset by 9.6 and 6.8 percentage points, respectively. Further generalization experiments are performed on the DOTA dataset. The experimental results show that the proposed algorithm exhibits significant advantages and potential for small-target detection tasks.

Key words: small target detection, Unmanned Aerial Vehicle (UAV), feature fusion, DWA-Head, DA-MPDIoU

摘要： 无人机（UAV）低空航拍技术在数据采集、安全监控以及地形测绘等多个领域中得到了广泛的应用，显著提高了作业效率。然而在无人机航拍目标检测中，检测目标往往存在尺寸较小、密集且分布不均等问题。为此，提出一种基于改进YOLOv8网络的目标检测算法TOD-YOLO。首先，在主干网络部分引入RFAConv下采样模块，在增加较少参数量的情况下为卷积核提供有效的注意力，使得网络在特征提取阶段聚焦于更小的目标。其次，提出特征融合模块CSPOKM以及一种新的特征融合路径，提高模型对小目标特征信息的提取能力，提升特征融合网络的性能。然后，基于深度可分离卷积（DWConv）和高效多尺度注意力（EMA）设计轻量级注意力检测头DWA-Head替代原有的检测头，在降低参数量的同时提升模型的目标检测精度。最后，提出可动态调节正负样本系数的损失函数DA-MPDIoU，为易漏检、难分类的小目标样本分配更高的权重，解决完整交并比（CIoU）在小目标密度大的复杂场景下容易产生误检和漏检的问题。实验结果表明，改进算法在VisDrone2019数据集上mAP@0.5与mAP@0.5:0.95指标较YOLOv8n分别提升9.6和6.8百分点，同时结合在DOTA数据集上的泛化实验，充分验证了该算法在小目标检测任务中具备显著优势与巨大潜力。

关键词: 小目标检测, 无人机, 特征融合, DWA-Head, DA-MPDIoU

CLC Number:

TP391

DENG Yuhui, DENG Yueming, HE Xin. Air-to-Ground Dense Small Target Detection Algorithm Based on Multi-scale Feature Fusion[J]. Computer Engineering, 2026, 52(11): 191-203.

邓宇辉, 邓月明, 何鑫. 基于多尺度特征融合的空对地密集小目标检测算法[J]. 计算机工程, 2026, 52(11): 191-203.

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