Semi-supervised Two-stage Remote Sensing Object Detection Method Based on Dense Teacher
李雨秋 1薛健 1吕科 2王泳3
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作者信息
1. 中国科学院大学工程科学学院,北京 100049
2. 中国科学院大学工程科学学院,北京 100049;鹏城实验室,广东 深圳 518055
3. 中国科学院大学人工智能学院,北京 100049
折叠
摘要
针对遥感图像中的有向物体检测任务,提出了一种基于半监督学习的密集区域卷积神经网络(Dense Region Convolutional Neural Network,D-RCNN)框架,以减少对大规模标注数据的依赖并提高检测精度.在该框架中,利用教师-学生模型通过稠密伪标签生成与一致性损失进行训练,结合伪标签学习与数据扰动,提升模型对无标注数据的有效利用率.针对长尾分布问题,引入了 Seesaw Loss以动态调整各类别权重,进一步优化模型性能.在DOTA数据集上进行的实验表明,D-RCNN在1％、2％、5％标注率下的检测精度AP5.分别较完全监督方法提升了 7.21％、8.02％和2.84％.在低标注率条件下,D-RCNN在多个主要类别上表现出显著的性能优势,验证了其在遥感场景下的有效性.
Abstract
For the task of detecting directed objects in remote sensing images,a semi-supervised-learning-based Dense Region Convolutional Neural Network(D-RCNN)framework is proposed to reduce reliance on large-scale labeled data and improve detection accuracy.In this framework,a teacher-student model is utilized for training through dense pseudo-label generation and consistency loss,and pseudo-label learning is combined with data perturbation to enhance the model's effective utilization of unlabeled data.To address the long-tail distribution problem,Seesaw Loss is introduced to dynamically adjust the class weights,further optimizing the model performance.Experiments conducted on the DOTA dataset show that the D-RCNN improves detection accuracy by 7.21％,8.02％,and 2.84％in terms of AP50 at labeling rates of 1％,2％,and 5％respectively compared to fully-supervised methods.Under low labeling rate conditions,the D-RCNN shows significant performance advantages across multiple major categories,validating its effectiveness in remote sensing scenarios.
关键词
半监督学习/遥感图像/有向物体检测/伪标签学习/一致性训练
Key words
semi-supervised learning/remote sensing images/directed object detection/pseudo-label learning/consistency training
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