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Infrared Tiny Object Detection Using Semi-Supervised Learning and Data Augmentation

delete2025-12-01
delete0
PRE
AI
B
Bin Zhang
L
Liangshun Wu *
Y
Yuguo Wang
L
Ling Peng
J
Juan Hu
D
Dawei Jiang
DOI：10.1142/S0218001425510322delete
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摘要

摘要

En 中文
针对红外小目标检测任务中数据不足和高标注成本的核心挑战，本文提出了一种融合生成对抗网络和半监督学习的创新方法。通过基于StyleGAN2设计小目标生成对抗网络（STGAN），结合自注意力机制和对比学习损失，有效提升了生成数据的质量和多样性。同时，利用知识蒸馏框架，通过教师模型生成的伪标签优化学生模型，显著提高了小样本场景下的检测性能。实验结果表明，所提方法在SIRST数据集上实现了0.709的nIoU（1%标注数据），FID指数降低至0.45，均达到当前最优水平。消融实验进一步验证了检测损失、自注意力模块和REP-PAN特征融合策略的关键作用，并为小目标检测领域的数据扩充和模型优化提供了新思路。
Keyword:
GAN
knowledge distillation
infrared image
small target detection
small sample

期刊

International Journal of Pattern Recognition and Artificial Intelligence 封面图
International Journal of Pattern Recognition and Artificial Intelligence
IF:
1.1
论文数:
215
被引数:
2.0K

机构

G
guizhou education university
学者数:
55
论文数: 21
被引数: 0
X
xinjiang university of political science & law
学者数:
17
论文数: 12
被引数: 0
引用论文

引用论文

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