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

Wenguang Tao, Xiaotian Wang, Tian Yan, Haixia Bi, Jie Yan

Abstract: 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.

External IDs:doi:10.1109/tgrs.2024.3510948