Regression-Guided Refocusing Learning with Feature Alignment for Remote Sensing Tiny Object Detection

计算机科学 目标检测 人工智能 特征(语言学) 遥感 特征提取 计算机视觉 模式识别(心理学) 对象(语法) 地质学 语言学 哲学
作者
Ling Ge,Guanqun Wang,Tong Zhang,Yin Zhuang,He Chen,Hao Dong,Liang Chen
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:: 1-1
标识
DOI:10.1109/tgrs.2024.3407122
摘要

Tiny object detection is a formidable challenge in remote sensing intelligent interpretation. Tiny objects are usually fuzzy, densely distributed and highly sensitive to positioning errors, which leads to the mainstream detector usually achieving suboptimal detection performance when facing tiny objects. To address the mismatch of mainstream detector architectures and model optimization strategies in the context of tiny object detection, this paper presents an efficient and interpretable algorithm for tiny object detection, termed the Cross-Attention based Feature Fusion Enhanced tiny object detection Network (CAF 2 ENet). First, the cross-attention mechanism is introduced to refine the upsampling results of deep features. This refinement improves the precision of multi-scale feature fusion. Second, a training strategy named regression-based refocusing learning is introduced. Deviating from the conventional optimization strategy, our method guides the optimizer to prioritize higher-quality detection boxes by adjusting sample weights. This adjustment significantly amplifies the detector's potential to achieve superior detection results. Finally, the object composite confidence score is employed for the interpretable filtering of detection boxes. Extensive experiments on Tiny Object Detection in Aerial Images (AI-TOD) and object Detection in Optical Remote sensing images (DIOR) datasets are carried out, and comparison indicate that the proposed CAF 2 ENet can perform the remarkable performance compared to other state-of-the-art (SOTA) tiny object detection detectors, as it can reach 63.7% Average Precision ( AP 50 ) on AI-TOD and 75.4% AP 50 on DIOR, achieve SOTA performance.
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