复杂环境下无人机航拍小目标检测算法

刘杰; 刘书豪; 田明; 崔志刚

doi:10.11999/JEIT251126

复杂环境下无人机航拍小目标检测算法

doi: 10.11999/JEIT251126 cstr: 32379.14.JEIT251126

刘杰^1, ,,
刘书豪¹,
田明²,
崔志刚³

1.
哈尔滨理工大学测控技术与通信工程学院哈尔滨 150080
2.
中国电信股份有限公司黑龙江分公司哈尔滨 150040
3.
黑龙江省公路建设中心哈尔滨 150001

基金项目: 黑龙江省自然科学基金(LH2023E086)，黑龙江省交通运输厅科技项目(HJK2024B002)

详细信息

作者简介:
刘杰：女，博士，副教授，研究方向为人工智能与图像处理、大数据模型及预测

刘书豪：男，硕士生，研究方向为深度学习与目标检测

田明：男，工程师，研究方向为大数据建模与数据预测

崔志刚：男，硕士，高级工程师，研究方向为智慧公路与道路养护

通讯作者:
刘杰　liujie@hrbust.edu.cn

中图分类号: TN911.7; TP391.4
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- 文章访问数: 456
- HTML全文浏览量: 250
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- 被引次数: 0
出版历程
- 收稿日期: 2025-10-27
- 修回日期: 2026-01-22
- 录用日期: 2026-01-22
- 网络出版日期: 2026-02-11
- 刊出日期: 2026-04-10

Small Object Detection Algorithm for UAV Aerial Images in Complex Environments

1.
School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2.
China Telecom Heilongjiang Branch, Harbin 150040, China
3.
Heilongjiang Provincial Highway Construction Center, Harbin 150001, China

Funds: The Natural Science Foundation of Heilongjiang Province (LH2023E086), The Science and Technology Project of Heilongjiang Provincial Communications Department (HJK2024B002)

摘要

摘要: 无人机航拍图像因其分辨率高、视角广、部署灵活的特点，在智能交通领域得到广泛应用。针对无人机航拍图像中目标尺度变化大、背景复杂、小目标密集等问题，该文提出一种面向复杂环境的无人机航拍目标检测算法HAR-DETR。首先，对骨干网络的最后两层BasicBlock重新设计，添加聚合感知注意力以提取目标的多尺度特征，增大感受野和对细粒度目标的感知效果；其次，设计高分辨率检测分支，提高模型对小目标检测的敏感度。最后，提出基于特征金字塔的重校准特征融合网络(RFF-FPN)，将小目标的浅层边界特征与深层语义特征结合，更好地捕捉多尺度目标的语义信息，同时简化颈部网络的结构。实验结果表明，在VisDrone2019数据集上，HAR-DETR算法的mAP₅₀相比原RT-DETR模型提升3.8%，mAP_50-95提升3.2%。在RSOD数据集上展现出良好的泛化性能，在小目标检测任务中表现优异，具有较强的实用价值和推广前景。
- 小目标检测 /
- DETR /
- 特征融合 /
- 航拍图像
Abstract: Objective Small object detection is critical in applications such as UAV (Unmanned Aerial Vehicle) inspection and intelligent transportation systems, where accurate perception of diminutive targets is essential for operational reliability and safety. It supports automated identification and tracking of challenging targets. However, the limited pixel size of small objects, combined with frequent occlusion and background integration, introduces strong background noise and leads to poor performance and high false-negative rates in existing detection models. To address these issues and to achieve high-performance and high-precision detection of small objects in complex scenes, this study proposes HAR-DETR, an enhanced version of the RT-DETR baseline model, designed to improve detection accuracy for small objects. Methods HAR-DETR is designed for small object detection in aerial images and integrates three major improvements: Aggregated Attention, RFF-FPN (Recalibrated Feature Fusion Network-FPN), and a high-resolution detection branch. In the backbone, Aggregated Attention strengthens the model’s focus on relevant features of small objects. By expanding the receptive field, the model captures detailed edge and texture information, improving multi-scale feature extraction. During feature fusion, RFF-FPN selectively integrates high- and low-level features to retain critical spatial information and context. This supports better reconstruction of edges and contours of small objects and improves localization and recognition, particularly when object details are partially obscured by cluttered backgrounds or variable lighting. The high-resolution detection branch (HRDB) emphasizes edge features of small objects, enhancing perception and improving robustness and precision. Results and Discussions The model is compared with commonly used object detection models, including YOLOv5, YOLOv8, and YOLOv10, using precision, recall, and mAP metrics to assess performance in small object detection. Experimental results show that HAR-DETR outperforms the comparative models on the VisDrone2019 dataset (Table 1). The mAP₅₀ and mAP_50-95 increase by 3.8% and 3.2%, respectively, relative to the baseline model (Table 2). These results demonstrate superior detection performance in aerial images under complex conditions. GradCAM heatmaps are used for comparative analysis and show consistent improvements across all proposed components compared with the baseline model (Fig. 6). In the generalization experiment, the VisDrone2019 validation set and RSOD dataset are evaluated under identical training settings. The results confirm that HAR-DETR maintains strong generalization across heterogeneous tasks (Tables 3 and 4). Conclusions This work addresses false positives and false negatives in small object detection for aerial images captured in complex environments by using HAR-DETR. Aggregated Attention is used in the backbone to expand the receptive field and improve global feature extraction. During feature fusion, the RFF-FPN structure strengthens feature representation. A high-resolution detection head further increases sensitivity to edge textures of small objects. Evaluation on the VisDrone2019 and RSOD datasets shows: (1) mAP₅₀ and mAP_50-95 improve by 3.8% and 3.2%, respectively, reaching 51.2% and 32.1%, which reduces false negatives and false positives; (2) HAR-DETR outperforms mainstream object detection models, confirming its effectiveness; (3) the model achieves high accuracy in cross-dataset training, demonstrating strong generalization. These results show that HAR-DETR has stronger semantic representation and spatial awareness, adapts well to varied aerial perspectives and target distributions, and provides a more versatile solution for UAV visual perception in complex environments.
- Small object detection /
- RT-DETR /
- Feature fusion /
- Aerial images

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图 1 RT-DETR网络结构

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图 2 HAR-DETR网络结构

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图 3 Aggregated Attention网络结构图

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图 4 高分辨率检测分支(虚线)与颈部网络结构

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图 5 SBA与RAU结构图

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图 6 改进前后热力图比较

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图 7 RT-DETR(左)与HAR-DETR(右)的检测效果对比图

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表 1 对比实验结果

算法名称	PM	GFLOPs	P(%)	R(%)	mAP₅₀(%)	mAP_50-95(%)	fps
YOLOv5m	25.2	64.0	52.1	41.2	41.6	25.0	124.5
YOLOv5l	53.1	134.7	54.5	42.9	43.8	26.7	83.0
YOLOv8m	25.8	78.7	53.1	41.2	41.9	25.4	114.5
YOLOv8l	43.5	164.9	55.0	42.7	43.8	26.6	86.2
YOLOv10m	16.7	63.4	54.2	40.9	42.1	26.2	110.3
YOLOv10l	25.7	126.4	55.3	42.5	44.3	27.6	82.6
YOLOv12m	20.1	67.2	53.7	42.0	43.4	26.3	112.9
YOLOv12l	26.3	88.6	56.1	43.0	44.9	27.8	85.1
Gold-YOLO-s	21.5	46.0	-	-	34.3	19.8	102.0
Efficient DETR	32.0	159.0	49.5	36.1	36.7	22.0	19.7
Deformable DETR	41.2	173.1	-	-	43.1	27.1	16.4
RT-DETR-R18	20.0	57.0	62.2	46.6	47.4	28.9	45.8
RT-DETR-R34	31.1	88.8	63.1	45.7	48.6	29.8	25.4
文献[15]	-	52.4	-	-	49.4	30.2	40.6
文献[16]	14.6	49.6	64.3	48.8	50.8	31.7	54.6
HAR-DETR	22.8	84.4	64.5	49.0	51.2	32.1	37.8

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表 2 消融实验结果(%)

模型	P	R	mAP₅₀	mAP_50-95
Baseline	62.2	46.6	47.4	28.9
+AA	62.0	46.2	47.7	29.3
+HRDB	61.9	46.8	48.8	30.6
+RFF-FPN	61.7	46.3	47.5	29.0
+RFF-FPN+HRDB	62.4	48.1	49.7	31.3
+AA+ HRDB	62.7	48.6	50.3	31.7
+AA+HRDB+RFF-FPN	64.5	49.0	51.2	32.1

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表 3 Visdrone2019测试集实验结果(%)

模型	P	R	mAP₅₀	mAP_50-95
Baseline	55.7	39.5	37.9	21.9
HAR-DETR	58.3	40.9	40.4	24.0

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表 4 RSOD数据集实验结果(%)

模型	P	R	mAP₅₀	mAP_50-95
Baseline	95.3	95.6	97.4	71.4
HAR-DETR	95.2	96.1	97.5	73.6

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