基于伪标记和二维高斯预测建模的红外小目标检测半监督学习框架

IF 0.8 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Jianlan Liu, Yingying Gao, Hui Bai
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引用次数: 0

摘要

红外小目标检测由于标记数据有限和背景干扰复杂,面临着很大的挑战。本文提出了一种半监督学习框架,该框架集成了伪标记和二维高斯预测建模来解决这些挑战。通过自适应伪标签生成利用未标记数据,该框架增强了模型泛化。在推理过程中引入了一种新的二维高斯预测模型来表征目标的空间分布,从而在噪声背景下实现精确定位。此外,相关感知损失函数通过加强振幅和空间扩散之间的物理约束来优化预测模型参数。在SIRST数据集上的实验显示了最先进的性能,与现有方法相比,f1得分提高了0.05,AP提高了4.9%。该框架为监视和遥感应用中的红外小目标检测提供了一个强大的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Semi-Supervised Learning Framework for Infrared Small Target Detection Using Pseudo-Labelling and Two-Dimensional Gaussian Prediction Modelling

A Semi-Supervised Learning Framework for Infrared Small Target Detection Using Pseudo-Labelling and Two-Dimensional Gaussian Prediction Modelling

A Semi-Supervised Learning Framework for Infrared Small Target Detection Using Pseudo-Labelling and Two-Dimensional Gaussian Prediction Modelling

A Semi-Supervised Learning Framework for Infrared Small Target Detection Using Pseudo-Labelling and Two-Dimensional Gaussian Prediction Modelling

A Semi-Supervised Learning Framework for Infrared Small Target Detection Using Pseudo-Labelling and Two-Dimensional Gaussian Prediction Modelling

Infrared small target detection faces significant challenges due to limited labelled data and complex background interference. This paper proposes a semi-supervised learning framework that integrates pseudo-labelling and two-dimensional Gaussian prediction modelling to address these challenges. By leveraging unlabelled data through adaptive pseudo-label generation, the framework enhances model generalisation. A novel two-dimensional Gaussian prediction model is introduced during inference to characterise target spatial distributions, enabling precise localisation under noisy backgrounds. Additionally, a correlation-aware loss function optimises the prediction model parameters by enforcing physical constraints between amplitude and spatial spread. Experiments on the SIRST dataset demonstrate state-of-the-art performance, achieving 0.05 higher F1-score and 4.9% higher AP compared to existing methods. This framework provides a robust solution for infrared small target detection in surveillance and remote sensing applications.

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来源期刊
Electronics Letters
Electronics Letters 工程技术-工程:电子与电气
CiteScore
2.70
自引率
0.00%
发文量
268
审稿时长
3.6 months
期刊介绍: Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews. Scope As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below. Antennas and Propagation Biomedical and Bioinspired Technologies, Signal Processing and Applications Control Engineering Electromagnetism: Theory, Materials and Devices Electronic Circuits and Systems Image, Video and Vision Processing and Applications Information, Computing and Communications Instrumentation and Measurement Microwave Technology Optical Communications Photonics and Opto-Electronics Power Electronics, Energy and Sustainability Radar, Sonar and Navigation Semiconductor Technology Signal Processing MIMO
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