跨域协同增强的遥感影像小目标检测

张天扬; 张向荣; 王冠淳; 唐旭

doi:10.11999/JEIT260317

跨域协同增强的遥感影像小目标检测

doi: 10.11999/JEIT260317 cstr: 32379.14.JEIT260317

西安电子科技大学人工智能学院西安 710126

基金项目: 国家自然科学基金 (62501433, 62506285, 62571387)，中央高校基础科研业务费(QTZX25070)，陕西省自然科学基础研究计划青年项目(2025JC-YBQN-795)

详细信息

作者简介:
张天扬：男，讲师，研究方向为遥感影像目标检测等，　zhangtianyang@xidian.edu.cn

张向荣：女，教授，研究方向为人工智能、遥感影像解译等，　xrzhang@mail.xidian.edu.cn

王冠淳：男，讲师，研究方向为遥感影像目标检测等

唐旭：男，教授，研究方向为人工智能、计算机视觉等

通讯作者:
张向荣　xrzhang@mail.xidian.edu.cn

中图分类号: TP751.2
计量
- 文章访问数: 31
- HTML全文浏览量: 16
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2026-03-18
- 修回日期: 2026-04-24
- 录用日期: 2026-07-06
- 网络出版日期: 2026-07-19

Cross-Domain Collaborative Enhancement for Tiny Object Detection in Remote Sensing Images

School of Artificial Intelligence, Xidian University, Xi’an 710126, China

Funds: Natural Science Foundation of China (62501433, 62506285, 62571387), Fundamental Research Funds for the Central Universities under Grant QTZX25070, Natural Science Basic Research Plan in Shaanxi Province of China (2025JC-YBQN-795)

摘要

摘要: 面对遥感影像中广泛分布的小目标，现有遥感影像目标检测方法仍面临严峻挑战，检测精度尚显不足。遥感影像小目标检测核心瓶颈在于小目标正样本匮乏与特征表示弱两大问题。因此，本文提出一种跨域协同增强的遥感影像小目标检测方法(Cross-Domain Collaborative Enhancement Detector, CDCEDet)，通过在空间域优化小目标标签分配，在频域提高小目标特征表示，以提升小目标检测性能。具体而言，本文首先设计尺度自适应锚框生成器，依据目标空间分布与几何尺度特性动态生成匹配锚框集合，有效缓解了锚框与小目标之间的尺度失配问题，显著提升了小目标正样本数量。在此基础上，提出分位数自适应标签分配机制，通过建模每个目标与其对应锚框的交并比统计分布动态调整标签分配阈值，有效缓解了固定阈值标签分配下的尺度偏差问题。此外，针对小目标特征表示弱的问题，从频域视角出发，构建频率自适应融合模块，利用自适应高通滤波器以增强小目标细节信息，并结合自适应低通滤波器维持上采样后小目标内部特征的语义一致性，从而提升小目标的特征表示能力。实验结果表明，在AI-TODv2数据集上，所提方法在AP₅₀和AP_50-95指标上较现有最优方法提升1.8%和0.7%；在AI-TOD-R数据集上，两项指标分别提升2.6%和0.7%，展现出良好的小目标检测能力和泛化能力。
- 小目标检测 /
- 遥感影像 /
- 标签分配 /
- 特征融合 /
- 频域学习
Abstract: Objective The rapid development of deep learning has significantly advanced object detection in remote sensing images (RSIs).Due to constraints imposed by imaging conditions and object size, a large number of tiny objects (with a pixel area of less than 16×16) are widely distributed in RSIs.However, compared with satisfactory detection performance on normal-scaled objects, current object detection methods exhibit a significant performance gap for tiny objects.This is primarily attributed to the two critical limitations: insufficient positive label assignment and weak feature representations.To address these issues, a novel Cross-Domain Collaborative Enhancement Detector (CDCEDet) is proposed.The CDCEDet jointly optimizes the label assignment in the spatial domain and strengthens the feature representations in the frequency domain for tiny objects, thereby establishing an accurate and robust framework for remote sensing tiny object detection. Methods The overall framework of the proposed CDCEDet is depicted in Fig.2 and comprises three key components.First, a Scale-Adaptive Anchor Generator (SAAG) is devised to dynamically generate anchors matched with the scales of ground-truth (GT) objects, effectively mitigating the scale mismatch issue neglected in previous works.Compared to conventional uniform-scale anchor generators, the proposed SAAG significantly increases positive samples for tiny objects, even under the IoU-threshold-based label assignment.Second, a Quantile-based Adaptive Label Assignment (QALA) mechanism is proposed to replace the fixed IoU threshold label assignment.The QALA exploits the IoU distribution between each GT object and its matched anchors to dynamically generate an adaptive threshold tailored to each GT object, thereby further boosting positive samples for tiny objects.Third, a Frequency-Adaptive Fusion (FAF) module is constructed to enhance feature representations from a frequency-domain perspective.On the one hand, adaptive high-pass filters are employed to highlight high-frequency information, which mitigates the information loss during channel compression.On the other hand, adaptive low-pass filters are applied to smooth and up-sample high-level features, thereby alleviating the feature inconsistency within the up-sampled objects. Results and Discussions Extensive experiments are conducted on two publicly available remote sensing tiny object detection datasets, AI-TODv2 and AI-TOD-R, by comparing the proposed method with several latest approaches, including RFLA, DCNet, and DFCL.On the AI-TODv2 dataset (Table 1), the proposed CDCEDet achieves performance gains of 1.8% and 0.7% in terms of AP₅₀ and AP_50-95, respectively, compared to the latest method.On the AI-TOD-R dataset (Table 2), AP₅₀ and AP_50-95 are improved by 2.6% and 0.7%, respectively.These results demonstrate that the proposed CDCEDet possesses superior performance and robust generalization capabilities for tiny object detection in RSIs.Ablation studies and configuration analyses of the CDCEDet core modules (Table 3, Table 4, Table 5, and Table 6) further verify the effectiveness of each proposed component and their complementary nature.Qualitative evaluations on both datasets (Fig.3) confirm that the proposed method accurately detects tiny objects in both sparse and dense distributions scenarios.Fig.4 confirms that the proposed SAAG generates scale-matched anchors for each object and assigns more positive samples to tiny objects than widely used uniformly distributed anchor generator.Furthermore, in comparative visualizations with RFLA and DCNet (Fig.5), CDCEDet exhibits superior detection accuracy and demonstrates a more effective reduction in missed detections. Conclusions This paper proposes a novel CDCEDet to address the insufficient label assignment and weak feature representations in remote sensing tiny object detection.Specifically, a SAAG is proposed to generate scale-matched anchors tailored to each GT object, significantly increasing the number of positive samples for tiny objects.Additionally, a QALA mechanism is devised that exploits the IoU distribution between GT objects and their matched anchors to dynamically adjust the assignment threshold, thereby effectively mitigating the scale bias induced by the fixed threshold.Finally, a FAF module is constructed from a frequency-domain perspective, which adopts the adaptive high-pass filters and low-pass filters to strengthen the feature representations for tiny objects.Extensive experiments on two tiny object detection benchmarks demonstrate the superior performance and robust generalization of the proposed CDCEDet.Future research will focus on enhancing model efficiency and real-time performance to facilitate the practical deployment of the proposed approach in remote sensing scenarios.
- Tiny object detection /
- Remote sensing images /
- Label assignment /
- Feature fusion /
- Frequency learning

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图 1 锚框-目标尺度比例r与IoU的变化关系

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图 2 跨域协同增强的遥感影像小目标检测方法整体结构图

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图 3 本文方法在AI-TODv2和AI-TOD-R数据集上可视化检测结果

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图 4 SAAG与均匀分布锚框的目标正样本分配可视化对比

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图 5 本文方法与其他方法的检测结果可视化对比

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表 1 AI-TODv2数据集上不同算法的对比实验结果

方法	AP_50-95	AP₅₀	AP₇₅	AP_vt	AP_t	AP_s	AP_m
Faster R-CNN*^[11]	12.8	29.9	9.4	0.0	9.2	24.6	37.0
FSANet*^[9]	17.6	45.0	10.5	5.4	15.8	22.9	33.8
LTDNet^[16]	18.9	46.7	12.1	5.3	17.7	24.2	34.3
NWD^[3]	21.4	53.2	12.5	7.7	20.7	26.8	35.2
MENet*^[17]	21.5	52.7	13.6	8.4	21.6	25.4	33.4
ADAS-GPM^[3]	22.3	53.7	13.5	7.1	21.9	27.5	35.1
RFLA*^[4]	22.9	55.7	14.7	8.5	22.4	28.7	36.4
DCEDet^[18]	23.5	53.9	16.8	8.5	24.1	28.2	37.1
DCNet^[19]	23.6	56.3	15.7	7.6	23.6	28.6	36.4
PGDP^[20]	23.9	55.8	16.6	7.3	23.5	29.0	-
CDCEDet(本文方法)	24.6	58.1	16.4	10.5	24.3	29.4	38.1
注：*表示基于官方代码复现结果，粗体表示最优结果，下划线表示次优结果

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表 2 AI-TOD-R数据集上不同算法的对比实验结果

方法	AP_50-95	AP₅₀	AP₇₅	AP_vt	AP_t	AP_s	AP_m
RoI Transformer*^[21]	12.3	35.3	5.5	1.3	10.6	19.7	24.5
Oriented R-CNN^[22]	11.2	33.2	4.3	0.6	9.1	19.5	23.2
ReDet^[23]	11.6	32.8	4.8	1.4	9.5	19.4	23.2
Oriented RepPoints^[24]	13.0	40.3	4.2	5.2	12.2	16.8	21.4
ARS-DETR^[25]	14.3	41.1	5.8	6.3	14.5	17.6	18.7
NWD*^[3]	15.4	46.9	5.4	5.9	14.2	20.4	24.4
RFLA*^[4]	16.0	48.2	5.6	5.9	14.8	20.1	24.1
DCFL^[13]	15.7	47.0	5.8	6.3	14.8	19.6	22.4
CDCEDet(本文方法)	16.7	50.8	5.7	6.8	15.7	21.6	25.3
注：*表示基于官方代码复现结果，粗体表示最优结果，下划线表示次优结果

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表 3 本文提出各个模块的消融实验结果

SAAG	QALA	FAF	AP_50-95	AP₅₀	AP₇₅	AP_vt	AP_t	AP_s	AP_m	参数量	GFLOPs	FPS
			12.6	29.5	8.8	0.0	8.4	25.6	37.1	41.16M	144.09	54.8
√			22.6	53.9	15.3	7.4	22.3	28.1	37.7	41.75M	144.18	45.0
√	√		23.6	56.1	15.8	9.3	23.1	29.0	37.0	41.75M	144.18	44.6
		√	14.0	32.5	9.8	0.0	10.6	26.4	37.8	47.31M	155.34	48.4
√	√	√	24.6	58.1	16.4	10.5	24.3	29.4	38.1	47.90M	155.43	40.5

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表 4 QALA机制中初始分位数阈值参数$ {a}_{0} $敏感性分析

$ {a}_{0} $	AP_50-95	AP_vt	AP_t	AP_s	AP_m
-	22.6	7.4	22.3	28.1	37.7
0.95	23.0	8.9	22.9	28.3	36.9
0.85	23.2	8.9	22.6	28.7	36.7
0.75	23.1	8.8	22.5	28.4	36.9
0.5	22.8	8.3	22.6	28.0	36.3
0.3	22.5	7.3	22.5	28.1	35.9
0.1	22.1	6.9	22.1	27.2	35.4

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表 5 QALA机制中分位数变化范围参数$ {a}_{1} $敏感性分析

$ {a}_{1} $	AP_50-95	AP_vt	AP_t	AP_s	AP_m
0	23.2	8.9	22.6	28.7	36.7
0.1	23.6	9.3	23.1	29.0	37.0
0.3	23.0	8.4	22.9	27.9	36.6
0.5	22.8	8.3	22.8	28.8	36.0
0.7	22.8	8.5	22.4	28.4	35.6

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表 6 不同滤波器核尺寸配置下的FAF模块消融实验结果

滤波器核尺寸		AP_50-95	AP_vt	AP_t	AP_s	AP_m
高通	低通	AP_50-95	AP_vt	AP_t	AP_s	AP_m
$ \hat{K}=3 $	$ \overline{K}=3 $	24.3	9.1	24.1	29.2	37.6
$ \hat{K}=5 $	$ \overline{K}=3 $	24.4	9.5	24.6	29.1	37.7
$ \hat{K}=5 $	$ \overline{K}=5 $	24.2	9.0	24.6	28.7	37.3
$ \hat{K}=3 $	$ \overline{K}=5 $	24.6	10.5	24.3	29.4	38.1

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