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24 PAPERS ARE CONTAINED IN THIS VOLUME UNDER THE FOLLOWING HEADINGS: MODELING;target detection;target classification;target TRACKING AND HANDOFF. TECHNICAL AND PROFESSIONAL PAPERS FROM THIS CONFERENCE ARE INDEXED WITH THE CONFERENCE CODE NO. 01637 IN THE EI ENGINEERING MEETINGS (TM) DATABASE PRODUCED BY ENGINEERING INFORMATION, INC.

关键词： infrared devices

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infrared technology FOR target detection AND classification(PROCEEDINGS), 1981.

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Proceedings of SPIE - The International Society for Optical Engineering 1982年 V 302卷

24 PAPERS ARE CONTAINED IN THIS VOLUME UNDER THE FOLLOWING HEADINGS: MODELING; target detection; target classification;target TRACKING AND HANDOFF. TECHNICAL AND PROFESSIONAL PAPERS FROM THIS CONFERENCE ARE INDEXED WITH THE CONFERENCE CODE NO. 01637 IN THE EI ENGINEERING MEETINGS (TM) DATABASE PRODUCED BY ENGINEERING INFORMATION, INC.

关键词： infrared DEVICES

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infrared technology for target detection and classification

Infrared Technology for Target Detection and Classification

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infrared technology for target detection and classification 1981

The proceedings contain 24 papers. The topics discussed include: infrared target array development;model for generating synthetic three-dimensional (3D) images of small vehicles;optical communications and laser beam acquisition performances;comparison of imaging infrared detection algorithms;target acquisition and extraction from cluttered backgrounds;image analysis using polarized Hough transform and edge enhancer;intensity correlation techniques for passive optical device detection;eliminating nearest neighbor searches in estimating target orientation;designing for stray radiation rejection;optimal performance limits for detection and classification algorithms;feature analysis for forward looking infrared (FLIR) target identification;automatic ship recognition using a passive radiometric sensor;and infrared ship classification using a new moment pattern recognition concept.

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MSFFNet: A Multilevel Sparse Feature Fusion Network for infrared Dim Small target detection

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 147-159页

作者： Ren, Xiangyang Jiao, Boyang Peng, Zhenming Kou, Renke Wang, Peng Li, Mingyuan Zhengzhou Univ Affiliated Hosp 1 Med 3D Printing Ctr Henan Prov Zhengzhou 450000 Peoples R China Zhengzhou Univ Clin Med Coll 1 Zhengzhou 450000 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Lab Imaging Detect & Intelligent Percept Chengdu 610054 Peoples R China Army Engn Univ Shijiazhuang Campus Shijiazhuang 050003 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Affiliated Hosp 1 Natl Engn Lab Internet Med Syst & Applicat Zhengzhou 450001 Peoples R China

The sparse characteristics of target features poses significant challenges when using deep learning methods for infrared dim small targets. To tackle this issue, this article proposes a novel multilevel sparse feature fusion network for detecting infrared dim small targets. A feature-level sparse feature fusion network fuses target features of the same level and different depths to express small target features. A decision-level sparse feature fusion network fuses features from different decision spaces to improve decision confidence. To enrich the feature representation of the target, different levels of target global features are introduced into the decision-level sparse feature fusion network. During the network training process, a deep joint supervision training strategy is proposed to supervise and train the multilevel sparse feature fusion network, aiming to fully learn the feature representation of the target. According to the experimental results, the proposed infrared dim small targets detection method outperforms existing popular methods under sparse target features.

关键词： Feature extraction Object detection Training Deep learning Noise Interference classification algorithms Accuracy YOLO Shape Decision-level sparse feature fusion deep joint supervision feature-level sparse feature fusion infrared small target

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Adaptive Feature-Manipulated Vehicle and Pedestrian detection in infrared Images

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第4期26卷 4579-4591页

作者： Chen, Guangchen Zhang, Pengcheng Zhang, Yinhui He, Zifen Shi, Benjie Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Peoples R China

infrared imaging is widely applied in assisted driving systems to enable night vision for sensitive targets such as vehicles and pedestrians. However, the detection accuracy of these targets is limited on lightweight detection networks due to their coarse color and texture characteristics rendered in infrared images. To solve this problem, we propose an adaptive feature-manipulated network (AFMNet) for accurate vehicle and pedestrian detection in infrared images. First, a refined spatial pooling module that uses one-dimensional convolution is proposed to establish local and channel feature mapping under different receptive fields so that different fine-grained features are fused. Second, the shuffle manipulation module is designed which includes slicing and shuffling to manipulate the spatial and channel features so that the information loss problem caused by conventional convolutional downsampling is overcome. Third, the adaptive connection of features at different scales using learnable parameters is proposed, and then the target features are reinforced by location and channel calibration branches. The experimental results show that AFMNet achieves the best performance in terms of average detection accuracy of 86.4%, model size of 5.3MB, and detection speed of 48FPS on GTX 2080Ti.

关键词： Feature extraction Pedestrians Accuracy Prediction algorithms Object detection Convolutional neural networks Semantics classification algorithms Deep learning Radio frequency Assisted driving infrared detection refined spatial pooling shuffle manipulation adaptive connection

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infrared technology for target detection and classification

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Journal of Modern Optics 1983年第4期30卷 411-412页

"infrared technology for target detection and classification."Optica Acta: International Journal of Optics, 30(4), pp. 411–412

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infrared spectral imaging-based image recognition for motion detection

International Journal of Intelligent Networks

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International Journal of Intelligent Networks 2025年 6卷 14-26页

作者： Li, Yong School of Mechanical and Electronic Engineering Zhongshan Polytechnic Guangdong Zhongshan528400 China

The current infrared imaging recognition methods are inadequate for real-time performance and accuracy for moving objects. Furthermore, they are subject to several constraints, which makes it challenging to recognize stationary and occluded objects. Experts have conducted comprehensive research on infrared imaging, including the development of contour-based infrared motion video image acquisition, the introduction of novel infrared image generation models that align with infrared imaging principles, and the formulation of innovative methods for the joint classification of spatial-spectral and hyper-spectral images. However, none of these advancements have been implemented for enhancement. In order to improve the infrared motion target detection technology, research on image recognition technology based on infrared spectral imaging, the establishment of infrared radiation characteristics model converted image, and combined with the local binary mode for motion target feature extraction, the construction of the background model, applied to the motion detection in the recognition of motion targets. The results demonstrated that the combination effect of local binary pattern feature extraction and analysis of feature vectors increased in accuracy and detection rate with the number of images. Compared to other algorithms, the research algorithm demonstrated a superior signal-to-noise ratio and gain amplitude. The unmanned aerial vehicle signal-to-noise ratio was 13.487, with a gain amplitude of 2.214, while the civil aviation aircraft signal-to-noise ratio was 6.369, with a gain amplitude of 1.792. Therefore, using infrared image feature vectors for image recognition is more effective in motion detection, providing valuable insights for improving the recognition and detection performance of infrared detection technology. © 2025 The Authors

关键词： Thermography (imaging)

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Near-infrared Spectral MEMS Gas Sensor for Multi-Component Food Gas detection

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MICROMACHINES 2025年第2期16卷 135-135页

作者： Yan, Xiaojian Tan, Yao Wang, Yi Chen, Gongdai Xia, Weigao Zhou, Gang Luo, Hongliang Liu, Hao Gong, Tianxun Zhang, Xiaosheng Univ Elect Sci & Technol China Sch Integrated Circuit Sci & Engn Chengdu 611731 Peoples R China Panovas Technol Co Ltd Chengdu 610041 Peoples R China

The complex application environments of gas detection, such as in industrial process monitoring and control, atmospheric and environmental monitoring, and food safety, require real-time and online high-sensitivity gas detection, as well as the accurate identification and quantitative analysis of gas samples. Despite the progress in gas analysis and detection methods, high-precision and high-sensitivity detection requirements for target gases of multiple components in mixed gases are still challenging. Here, we demonstrate a micro-electromechanical system (MEMS) with near-infrared (NIR) spectral gas detection technology and spectral model training, which is used to improve the detection and classification of multi-component gases in food. During blind sample testing, the NIR spectral gas sensor demonstrated over 90% accuracy in identifying mixed gases, as well as achieving the classification of ethanol concentration. We envision that our design strategy of an NIR spectral gas sensor could enhance the gas detection and distinguishing ability under the conditions of background gas interference and cross-interference in multi-component detection.

关键词： MEMS micro-electromechanical systems near-infrared spectroscopy gas sensor

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Dim moving target detection algorithm based on spatio-temporal classification sparse representation

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infrared PHYSICS & technology 2014年 67卷 273-282页

作者： Li, Zhengzhou Dai, Zhen Fu, Hongxia Hou, Qian Wang, Zhen Yang, Lijiao Jin, Gang Liu, Changju Li, Ruzhang Chongqing Univ Coll Commun Engn Chongqing 400044 Peoples R China China Aerodynam Res & Dev Ctr Mianyang 621000 Sichuan Peoples R China China Elect Technology Grp Corp Sichuan Inst Solid State Circuits Res Inst 24 Natl Lab Analogue Integrated Circuits Chongqing 400060 Peoples R China

A dim moving target detection algorithm based on spatio-temporal classification sparse representation, which can characterize the motion information and morphological feature of target and background clutter, is proposed to enhance the performance of target detection. A spatio-temporal redundant dictionary is trained according to the content of infrared image sequence, and then is subdivided into target spatio-temporal redundant dictionary describing moving target, and background spatio-temporal redundant dictionary embedding background by the criterion that the target spatio-temporal atom could be decomposed more sparsely over Gaussian spatio-temporal redundant dictionary. The target and background clutter can be sparsely decomposed over their corresponding spatio-temporal redundant dictionary, yet could not be sparsely decomposed on their opposite spatio-temporal redundant dictionary, and so their residuals after reconstruction by the prescribed number of target and background spatio-temporal atoms would differ very visibly. Some experimental results show this proposed approach could not only improve the sparsity more efficiently, but also enhance the target detection performance more effectively. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Dim target detection Spatio-temporal classification redundant dictionary target spatio-temporal redundant dictionary Background spatio-temporal redundant dictionary Signal sparse reconstruction

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An enhance ranging algorithm based on multi-waveform classification with hyperspectral LiDAR

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MEASUREMENT 2025年 253卷

作者： Li, Xingyun Shao, Hui Lu, Yuan Chen, Yuwei Sun, Long Dai, Hui Hong, Tao Zhang, Xinyuan Zhang, Hulong Anhui Jianzhu Univ Anhui Int Joint Res Ctr Ancient Architecture Intel Hefei 230601 Peoples R China Infrared & Low Temp Plasma Key Lab Anhui Prov Hefei 230037 Peoples R China Adv Laser Technol Lab Anhui Prov Hefei 230037 Peoples R China Anhui Jianzhu Univ Anhui Prov Engn Res Ctr Reg Environm Hlth & Spatia Hefei 230601 Peoples R China

When the light detection and ranging (LiDAR) detected system interrogates targets with high retro-reflective properties, the backscattered pulse energy is prone to exceed the maximum receiving range of the photodetector. It will cause signal saturation because the system cannot record the complete echo waveform, which leads to the deviation of ranging results. To solve the problem, this study proposes an enhanced ranging algorithm based on multi-waveform classification (RMWC) with 101-band hyperspectral LiDAR (HSL). Considering the difference of echo waveforms collected in different bands, the threshold method is combined with random forest classifier to classify echo waveforms into normal waveform, saturated waveform and U-shaped waveform. Then peak value method (PV), centroid method (CM) and waveform fitting method (WF) are selected to calculate the corresponding time of flight of the three waveforms respectively. Finally, the target distance is determined by the weighted average of the mode values of the bands' ranging results. Compared with PV, CM and WF ranging results at 905 nm, the experimental results verify that the proposed algorithm can effectively reduce the ranging error caused by waveform saturation. For planar retro-reflective target, the optimal average error (AE) and standard deviation (Std) are 0.0053 m and 0.0083 m, respectively. For diffuse reflection whiteboard, the AE is 0.0010 m and the Std is 0.0053 m. The reconstruction results of planar target point cloud show that RMWC ranging results are better than these traditional single-band ranging methods, which can provide a reference for the optimal design of high precision laser ranging system.

关键词： Laser ranging Saturated waveform Multi-waveform classification Waveform time discrimination

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