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

ZHANG Tianyang; ZHANG Xiangrong; WANG Guanchun; TANG Xu

doi:10.11999/JEIT260317

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ZHANG Tianyang, ZHANG Xiangrong, WANG Guanchun, TANG Xu. Cross-Domain Collaborative Enhancement for Tiny Object Detection in Remote Sensing Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260317

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ZHANG Tianyang, ZHANG Xiangrong, WANG Guanchun, TANG Xu. Cross-Domain Collaborative Enhancement for Tiny Object Detection in Remote Sensing Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260317

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

doi: 10.11999/JEIT260317 cstr: 32379.14.JEIT260317

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

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

Received Date: 2026-03-18
Accepted Date: 2026-07-06
Rev Recd Date: 2026-04-24

Available Online: 2026-07-19

Abstract

Abstract

Objective Deep learning has substantially advanced object detection in Remote Sensing Images (RSIs). However, because of imaging conditions and the inherently small size of many objects, a large proportion of targets in RSIs occupy fewer than 16 × 16 pixels. Therefore, current object detection methods achieve substantially lower detection accuracy for tiny objects than for normal-scale objects. This limitation primarily arises from two critical factors: insufficient positive sample assignment and weak feature representation. To address these challenges, a Cross-Domain Collaborative Enhancement Detector (CDCEDet) is proposed. CDCEDet jointly optimizes label assignment in the spatial domain and enhances feature representation in the frequency domain, thereby improving the accuracy and robustness of tiny object detection in RSIs. Methods The overall framework of CDCEDet is illustrated in Fig. 2 and consists of three major components. First, a Scale-Adaptive Anchor Generator (SAAG) is designed to dynamically generate anchors that match the scales of ground-truth (GT) objects, thereby effectively alleviating the scale mismatch between anchors and tiny objects that has been largely overlooked in previous studies. Compared with conventional uniformly distributed anchor generators, SAAG substantially increases the number of positive samples assigned to tiny objects, even under Intersection over Union (IoU)-based label assignment. Second, a Quantile-based Adaptive Label Assignment (QALA) mechanism is developed to replace the conventional fixed IoU threshold-based label assignment. QALA models the IoU distribution between each GT object and its matched anchors to generate an adaptive label assignment threshold for each object, thereby further increasing the number of positive samples assigned to tiny objects. Third, a Frequency-Adaptive Fusion (FAF) module is developed to enhance feature representation from a frequency-domain perspective. An adaptive high-pass filter is used to strengthen high-frequency details and compensate for information loss caused by channel compression, whereas an adaptive low-pass filter preserves semantic consistency during feature upsampling, thereby reducing semantic inconsistency within upsampled objects. Results and Discussions Extensive experiments are conducted on two public remote sensing tiny object detection datasets, AI-TODv2 and AI-TOD-R. The proposed method is compared with several state-of-the-art methods, including RFLA, DCNet, and DCFL. On the AI-TODv2 dataset (Table 1), CDCEDet improves AP₅₀ and AP_50–95 by 1.8% and 0.7%, respectively, compared with the best existing 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 CDCEDet achieves superior detection performance and strong generalization capability for tiny object detection in RSIs. Ablation studies and parameter analyses of the proposed modules (Tables 3–6) further verify the effectiveness of each component and their complementary contributions. Qualitative results on both datasets (Fig. 3) show that the proposed method accurately detects tiny objects in both sparse and dense scenes. As illustrated in Fig. 4, SAAG generates scale-matched anchors for individual objects and assigns substantially more positive samples to tiny objects than the conventional uniformly distributed anchor generator. Furthermore, visual comparisons with RFLA and DCNet (Fig. 5) demonstrate that CDCEDet achieves higher detection accuracy while substantially reducing missed detections. Conclusions A CDCEDet is proposed to address insufficient positive sample assignment and weak feature representation in remote sensing tiny object detection. Specifically, SAAG dynamically generates anchors that match the scales of GT objects, substantially increasing the number of positive samples assigned to tiny objects. QALA further improves label assignment by modeling the IoU distribution between GT objects and their matched anchors to adaptively determine the label assignment threshold, thereby effectively reducing the scale bias introduced by fixed IoU thresholds. In addition, FAF enhances feature representation from a frequency-domain perspective through an adaptive high-pass filter and an adaptive low-pass filter. Experimental results on two benchmark datasets demonstrate the superior detection performance and strong generalization capability of CDCEDet. Future work will focus on improving model efficiency and real-time performance to facilitate practical deployment in remote sensing applications.
- Tiny object detection,
- Remote sensing images,
- Label assignment,
- Feature fusion,
- Frequency-domain learning

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References(25)

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