GNSRNet: Geometric Guided Noise Reduction Super-Resolution Network for Remote Sensing Tiny Object Detection

Abstract

Tiny object detection in remote sensing typically faces the challenges of being submerged in backgrounds, limited feature representation, and high sensitivity to prediction errors due to the small size and diverse shapes. To address these challenges, a geometric guided noise reduction super-resolution (SR) network is proposed. First, a dual-branch denoising and SR feature pyramid network is proposed, which integrates an adaptive dynamic noise reduction module and an inference decoupled auxiliary SR branch, while a progressive loss-annealing strategy is further introduced to reduce reliance on the SR branch during inference, meeting the requirements of lightweight and high-performance remote sensing tiny object detection. Second, a geometric characteristic regression metric is proposed, which comprehensively considers the location accuracy and the shape similarity between the prediction and ground truth boxes, thereby improving bounding-box quality and detection precision. Extensive experiments have been conducted on the remote sensing tiny object datasets AI-TOD v1, AI-TOD v2, USOD, and VisDrone. Specifically, it reaches an AP of 31.6 on AI-TOD v1, 30.5 on AI-TOD v2, 37.4 on USOD, and 30.5 on the VisDrone, demonstrating its capability for tiny object detection in remote sensing.