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infrared small target detection based on spatial attention density peaks searching

Optoelectronics Letters 2025年第2期21卷 90-96页

作者： RAO Junmin LU Siyu LIU Shijian LI Fanming CAS Key Laboratory of Infrared System Detection and Imaging Technology Shanghai Institute of Technical PhysicsShanghai 200083China University of Chinese Academy of Sciences Beijing 100049China Shanghai Institute of Technical Physics Chinese Academy of SciencesShanghai 200083China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China

The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems,as they must strike a delicate balance between accuracy and *** this paper,we propose a detection algorithm based on spatial attention density peaks searching (SADPS) and an adaptive window selection ***,the difference-of-Gaussians (DoG) filter is introduced for preprocessing raw infrared ***,the image is processed by ***,an adaptive window selection scheme is applied to obtain window templates for the target scale ***,the small target feature is used to enhance the target and suppress the ***,the true targets are segmented through a *** experimental results show that compared with the seven state-of-the-art small targets detection baseline algorithms,the proposed method not only has better detection accuracy,but also has reasonable time consumption.

关键词： template searching infrared

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infrared aircraft few-shot classification method based on cross-correlation network

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红外与毫米波学报 2025年第1期44卷 103-111页

作者： HUANG Zhen ZHANG Yong GONG Jin-Fu Key Laboratory of Infrared System Detection and Imaging Technology Chinese Academy of SciencesShanghai 200083China University of Chinese Academy of Sciences Beijing 100049China Shanghai Institute of Technical Physics Chinese Academy of SciencesShanghai 200083China

In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is *** method combines two core modules:a simple parameter-free self-attention and *** analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot *** proposed cross-correlation network integrates these two modules and is trained in an end-to-end *** simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between *** with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target *** results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.

关键词： infrared imaging aircraft classification few-shot learning parameter-free attention cross attention

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Target detection enhancement method through multi-dimensional information differential projection and fusion

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Optics Express 2025年第10期33卷 21116-21131页

作者： Dong, Xue Lan, Yubo Zhao, Zongmin Cai, Chang Zhou, Zhiming Zhang, Tong Han, Pingli Xiang, Meng Liu, Jinpeng Xie, Peiyue Liu, Fei School of Optoelectronic Engineering Xidian University Xi’an 710071 China Xi’an Key Laboratory of Computational Imaging Xi’an 710071 China National Key Laboratory of Infrared Detection Technologies Shanghai 200083 China Beijing Key Laboratory of Advanced Optical Remote Sensing Technology Beijing 100094 China

As a powerful detection tool, spectral polarization imaging (SPI) reveals spatial, spectral, and polarization characteristics. When combined with deep learning, its detection capabilities can be further improved. However, SPI requires the acquisition and processing of large-scale data, and it is still facing significant challenges in detecting targets in complex scenes with reduced contrast, loss of spatial structures, and cluttered backgrounds. Hence, an innovative target detection enhancement method via multi-dimensional information differential projection and fusion is presented based on our self-developed SPI systems. It primarily comprises a target retrieval algorithm based on angle of polarization purification and a novel projection and fusion strategy, which can effectively exploit, enhance, and utilize the multi-dimensional distinctions between the target and background. The resulting fused image visualizes these distinctions, thereby highlighting the target from its background. Experiment results demonstrate the efficacy of our method in detecting targets with shielding, camouflage and shape loss in complex natural environments, i.e., grasslands, woodlands, and sky. This significantly enhances scenario comprehension and detection efficiency without extensive computation or model training, making it especially advantageous for military reconnaissance applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Color spaces Deep learning imaging techniques Machine learning Polarimetric imaging Underwater imaging

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Feature Enhancement Network for infrared Small Target Detection in Complex Backgrounds Based on Multi-Scale Attention Mechanism

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Applied Sciences (Switzerland) 2025年第9期15卷 4966-4966页

作者： Zhang, Sen Du, Weilin Liu, Yuan Zhou, Ni Li, Zheng Key Laboratory of Infrared System Detection and Imaging Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China University of Chinese Academy of Sciences Beijing100049 China

The identification of tiny objects via single-frame infrared is a significant challenge in computer vision, primarily due to large variances in target dimensions, overcrowded backgrounds, suboptimal signal-to-noise ratios, the propensity of bounding box regression to vary with target size, and potential partial occlusion scenarios. Addressing these challenges, we propose a sturdy network for enhancing features in the infrared detection of small targets utilizing multi-scale attention. In particular, the introduction of the Iterative Attentional Feature Fusion (iAFF) module at the detection network’s neck aims to tackle the issue of minor target features being overshadowed in the process of cross-scale feature fusion. Additionally, we present the Occlusion-Aware Attention Module (OAAM), which demonstrates greater tolerance for target localization errors in regions where local features are missing due to partial occlusion. By combining the scale and spatial attention mechanisms of the Dynamic Head, our approach adaptively learns the relative importance of different semantic layers. Furthermore, the integration of Normalized Wasserstein–Gaussian Distance (NWD) aims to tackle the convergence issues associated with the increased sensitivity of bounding box regression in identifying minor infrared targets. For assessing our technique’s efficiency, we present a novel benchmark dataset, IRMT-UAV, noted for its considerable discrepancies in target size, intricate backdrops, and substantial variations in the signal-to-noise ratio. The outcomes of our experiments using the public IRSTD-1k dataset and the internally developed IRMT-UAV dataset reveal that our technique surpasses cutting-edge (SOTA) methods, with mAP50 enhancements of 1.4% and 4.9%, respectively, thus proving our method’s efficiency and sturdiness. © 2025 by the authors.

关键词： Radar target recognition

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Edge-Object Co-Driven Learning for Remote Sensing Change Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Yangguang Liu, Fang Liu, Jia Xiao, Liang Nanjing University of Science and Technology School of Computer Science and Engineering Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing 210094 China Jiangsu Province Engineering Research Center of Airborne Detecting and Intelligent Perceptive Technology Nanjing 210049 China

Remote sensing change detection (CD) aims to accurately reveal surface changes by comparing two temporally separated images of the same area. However, in complex environments, insufficient edge detail recognition and limited feature extraction often affect the accuracy of CD. For this purpose, we propose a novel method named the edge-object co-driven learning network (EOCLNet), which employs a combination of the Pyramid Vision Transformer (PVT) and the Fast Segment Anything Model (FastSAM) as parallel feature extractors to capture rich multilevel features. Specifically, it includes three key components which are the edge extraction module (EEM), the object revelation module (ORM), and the edge-object learning (EOL). EEM explicitly captures edge details by combining low-level spatial features with high-level semantic features, providing essential edge knowledge. ORM reveals changed objects by aggregating the highest two levels of semantic features, providing initial change guidance. EOL is designed to implicitly mine edge clues by establishing relationships between edges and changed objects across multiple levels, receiving outputs from both EEM and ORM. Furthermore, during the training process, the uncertainty from the previous level’s change map is utilized to guide the learning at the next level, thereby achieving a transition from uncertainty to certainty. The effectiveness of EOCLNet is validated on three public datasets, where it outperforms several state-of-the-art CD methods. © 1980-2012 IEEE.

关键词： Change detection (CD) edge extraction edge-object learning (EOL) object revelation remote sensing

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Wafer bow control of extended wavelength InGaAs photodetector materials by strain tailoring

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Materials Science in Semiconductor Processing 2025年 197卷

作者： Liu, Bowen Gu, Yi Yin, Lihao Huang, Huijuan Li, Kang Fan, Liuyan Zhou, Hong Chen, Pingping Yu, Chunlei Li, Tao Shao, Xiumei Li, Xue Gong, Haimei National Key Laboratory of Infrared Detection Technologies Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China Key Laboratory of Infrared Imaging Materials and Detectors Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China Wuxi Zhongke Dexin Sensing Technology Co. Ltd. Jiangsu Wuxi 214135 China University of Chinese Academy of Sciences Beijing 100049 China

The wafer bow caused by lattice mismatch between metamorphic InGaAs epitaxial layer and InP substrate has become one of the key factors limiting the large-scale development of extended wavelength InGaAs focal plane array photodetectors. By introducing tensile strain in InAlAs buffer layers and InAlAs cap layer to compensate the compressive strain caused by lattice mismatch, the wafer bow was decreased from 55.9 μm to 22.2 μm. The influence of strain compensation structure on the anisotropy of metamorphic InGaAs materials was investigated by stress distribution, surface morphology, and PL uniformity. XRD and PL characterizations indicate that small tensile strain in InAlAs buffer layers did not reduce the quality of metamorphic InGaAs epitaxial material. This study shows that strain compensation is an effective method to control wafer bow and decrease anisotropy of metamorphic InGaAs materials grown on InP substrate. © 2025

关键词： Anisotropy Metamorphic InGaAs photodetector materials Strain compensation Wafer bow

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An alternative application of GaAs-based light-emitting diodes: X-ray detection and imaging

arXiv

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arXiv 2025年

作者： Yu, Quan Wang, Fangbao Yuan, Xin Liu, Ying Gan, Lianghua Xu, Gangyi Shen, Wenzhong Chen, Liang Zhang, Yueheng Key Laboratory of Artificial Structures and Quantum Control School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Radiation Detection Research Center Northwest Institute of Nuclear Technology Xi’an710024 China Key Laboratory of Infrared Imaging Materials and Detectors Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China

GaAs-based light-emitting diodes (LEDs) are commonly employed in a variety of applications, including medical imaging, biosensing, optical communications, and night vision. In this paper, we present an alternative application of GaAs-based LED with SI-GaAs substrate for X-ray detection and imaging. The mechanism relies on the semiconductor frequency down-conversion process, where the SI-GaAs substrate acts as a photodetector (PD). Upon X-ray irradiation, the generated photocurrent by the SI-GaAs substrate drives the LED to emit NIR photons which can be detect by a low-cost CCD. We demonstrate direct X-ray detection and present preliminary imaging results, providing another example of the applicability of the PD-LED design for optical frequency conversion. The proposed LED X-ray detector leverages mature materials and fabrication processes. The application of the frequency down-conversion concept makes it possible for pixel-less imaging using a large single imaging unit, eliminating the need for readout circuits. This PD-LED architecture offers an alternative approach to direct X-ray detection and imaging, characterized by higher absorption, improved image resolution, and enhanced material stability. Copyright © 2025, The Authors. All rights reserved.

关键词： III-V semiconductors

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Curvature Compensated CMOS Bandgap Reference with Novel Process Variation Calibration Technique

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Journal of Beijing Institute of technology 2018年第2期27卷 182-188页

作者： Jiancheng Zhang Mao Ye Yiqiang Zhao Gongyuan Zhao Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology Tianjin UniversityTianjin 300072China Tianjin Infrared Imaging Technology Engineering Center Tianjin 300072China

A lowtemperature coefficient（ TC） bandgap reference（ BGR） with novel process variation calibration technique is proposed in this paper. This proposed calibration technique compensating both TC and output value of BGR achieves fine adjustment step towards the reference voltage,while keeping optimal TC by utilizing large resistance to help layout match. The high-order curvature compensation realized by poly and p-diffusion resistors is introduced into the design to guarantee the temperature characteristic. Implemented in 180 nm technology,the proposed BGR has been simulated to have a power supply rejection ratio（ PSRR） of 91 dB@100 Hz. The calibration technique covers output voltage scope of 0. 49 V-0. 56 Vwith TC of 9. 45 × 10^（-6）/℃-9. 56 × 10^（-6）/℃ over the temperature range of-40 ℃-120 ℃. The designed BGR provides a reference voltage of 500 mV,with measured TC of 10. 1 × 10^（-6）/℃.

关键词： bandgap reference voltage process variation resistance-trimming current-calibration curvature compensation temperature coefficient

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Short-wave infrared InGaAs photodetectors and focal plane arrays

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Chinese Physics B 2018年第12期27卷 57-63页

作者： Yong-Gang Zhang Yi Gu Xiu-Mei Shao Xue Li Hai-Mei Gong Jia-Xiong Fang Key Laboratory of Infrared Imaging Materials and Detectors Shanghai Institute of Technical PhysicsChinese Academy of Sciences State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences

In this article, unique spectral features of short-wave infrared band of 1 μm–3 μm, and various applications related to the photodetectors and focal plane arrays in this band, are introduced briefly. In addition, the different material systems for the devices in this band are outlined. Based on the background, the development of lattice-matched and wavelengthextended InGaAs photodetectors and focal plane arrays, including our continuous efforts in this field, are reviewed. These devices are concentrated on the applications in spectral sensing and imaging, exclusive of optical fiber communication.

关键词： InGaAs short-wave infrared photodetectors focal plane arrays

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InP-based In_xGa_(1-x)As metamorphic buffers with different mismatch grading rates

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Journal of Semiconductors 2013年第7期34卷 42-46页

作者：方祥顾溢陈星佑周立曹远迎李好斯白音张永刚 State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences Key Laboratory of Infrared Imaging Materials and Detectors Chinese Academy of Sciences

Linearly graded In_xGa_（1-x）As metamorphic buffers with different mismatch grading rates were grown on InP substrate by gas source molecular beam *** temperature photoluminescence spectra show that the sample with lower mismatch grading rate in the buffer has stronger photoluminescence signal,indicating the improved optical *** force microscope images show that the lower mismatch grading rate in the buffer leads to a slightly rougher *** relaxation procedure with two steps in the buffer layers has been observed by X-ray diffraction reciprocal space *** measurements of X-ray diffraction also reveal that the lower mismatch grading rate in the buffer is beneficial for the lattice relaxation and release of residual *** further increase the relaxation degree,a lower mismatch grading rate and composition ＂overshoot＂ are suggested.

关键词： InxGa1-x As gas source molecular beam epitaxy photoluminescence reciprocal space mapping

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