DCEDet: Tiny Object Detection in Remote Sensing Images Based on Dual-Contrast Feature Enhancement and Dynamic Distance Measurement

Recent advances in deep learning have significantly improved remote sensing object detection (RSOD). However, tiny object detection (TOD) remains challenging due to two main issues: (1) limited appearance cues and (2) the traditional Intersection over Union (IoU)-based label assignment strategy, which struggles to identify enough positive samples. To address these, we propose DCEDet, a new tiny object detector for remote sensing images that enhances feature representation and optimizes label assignment. Specifically, we first design a dual-contrast feature enhancement structure, i.e., the Group-Single Context Enhancement Module (GSCEM) and Global-Local Feature Fusion Module (GLFFM). Among them, the GSCEM is designed to extract contextual enhancement features as supplementary information for TOD. The GLFFM is a feature fusion module devised to integrate both global object distribution and local detail information, aiming to prevent information loss and enhance the localization of tiny objects. In addition, the Normalized Distance and Difference Metric (NDDM) is designed as a dynamic distance measurement that enhances class representation and localization performance in TOD, thereby optimizing the training process. Finally, we conduct extensive experiments on two typical tiny object datasets, i.e., AI-TODv2 and LEVIR-SHIP, achieving optimal results of 27.8% APt and 81.2% AP50. The experimental results demonstrate the effectiveness and superiority of our method.

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