EDADet: Encoder-Decoder Domain Augmented Alignment Detector for Tiny Objects in Remote Sensing Images

doi:10.1109/TGRS.2024.3510948

EDADet: Encoder-Decoder Domain Augmented Alignment Detector for Tiny Objects in Remote Sensing Images

2025-01-01

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DOI：10.1109/TGRS.2024.3510948

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In recent years, deep learning has shown great potential in object detection applications, but it is still difficult to accurately detect tiny objects with an area proportion of less than 1% in remote sensing images. Most existing studies focus on designing complex networks to learn discriminative features of tiny objects, usually resulting in a heavy computational burden. In contrast, this article proposes an accurate and efficient single-stage detector called EDADet for tiny objects. First, domain conversion technology is used to realize cross-domain multimodal data fusion based on single-modal data input. Then, a tiny object-aware backbone is designed to extract features at different scales. Next, an encoder-decoder feature fusion (EDFF) structure is devised to achieve efficient cross-scale propagation of semantic information. Finally, a center-assist loss and an alignment self-supervised loss are adopted to alleviate the position sensitivity issue and drift of tiny objects. A series of experiments on the AI-TODv2 dataset demonstrate the effectiveness and practicality of our EDADet. It achieves state-of-the-art (SOTA) performance and surpasses the second-best method by 9.65% in AP50 and 4.86% in mAP.

Keywords:

Feature extraction

Training

Object detection

Detectors

Data augmentation

Costs

Sensitivity

Remote sensing

Interference

Data mining

Cross-domain multimodality

encoder-decoder feature fusion (EDFF)

loss function

remote sensing image

tiny object detection (TOD)

EDADet: Encoder-Decoder Domain Augmented Alignment Detector for Tiny Objects in Remote Sensing Images

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