Learning from the Best: High-Quality Sample Guidance for Oriented Object Detection

Yuying Pan, Qin Wu, Hui Shen, Zeda Chen, Yin Liu, Shengnan Fan, Haojie Zhou

Abstract: Oriented object detection (OOD) in remote sensing imagery has rapidly advanced in recent years. However, the complex characteristics of both objects and backgrounds still pose significant challenges, resulting in miss detections and false detections. Specifically, small objects and extreme aspect ratio objects are easily assimilated into the background. To solve these problems, we propose a High-Quality Sample Guidance Network (HQSGNet). Inspired by the human tendency to improve themselves through learning from outstanding individuals, we design a High-Quality Sample Guidance (HQSG) branch. This branch selects samples with high classification confidence and accurate regression to guide low-quality samples to optimize feature learning. Furthermore, to obtain more representative high-quality samples, we also propose a Discriminative Feature Refinement (DFR) module. This module provides a robust foundation for the selection of high-quality samples by reinforcing key feature information while suppressing irrelevant noise. Extensive experiments on three challenging remote sensing datasets (DOTA, DIOR-R and HRSC2016) achieve mAPs of 77.12%, 65.25% and 96.81% respectively, thereby validating the effectiveness of high-quality sample guidance mechanism in oriented object detection.

External IDs:dblp:conf/ijcnn/PanWSCLFZ25