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infrared technology for target detection and classification 1981

作者： McIntire, Thomas O. Scott, Edward A. Methodology and Instrumentation Division U.S. Army Yuma Proving Ground STEYP-MMI YumaAZ85364 United States

A "life size" thermal target array has been developed to facilitate in-flight testing of airborne weapon systems containing night vision subsystems. This in-flight testing to measure the performance of the night vision subsystem and its effect on overall weapon system performance is essential to the test and evaluation process of the particular weapon under test. This measurement of subsystem performance is called the Modulation Transfer Function, or MTF. In addition, a laser designator subsystem is frequently incorporated in a precision guided munition weapon system. In the test and evaluation of the designator, such quantities as beam quality (energy distribution), beam divergence, and beam wander are of interest. The thermal targets may be used to evaluate armored weapon systems. The capability of providing carefully controlled and variable thermal signatures in a field test environment is considered unique. The thermal target array consists of three targets: A six bar recognition target, a two bar detection target, and a laser designator scoring board (cross-hair). The image dimensions of 2.3 meters by 2.3 meters were derived from an optimized threat envelope. The thermal signatures of the targets are controllable to within 0.3 C about a differential setpoint. This differential setpoint is measured between the active element and the target background (or "ambient"). Several differential temperature settings are available to the test officer: 1.25°C, 3°C, 5°C, 7.5°C, and 10°C. This paper reviews the thermal array test objectives, target array fabrication, methodology of target utilization, and representative results. © 1982 SPIE. All rights reserved.

关键词： Vision

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STAR BACKGROUND CANCELLATION FOR DEEP SPACE SURVEILLANCE

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1981年第2期17卷 314-319页

作者： JOHNSON, CR SENTOVICH, MF HO, CQ Space Systems Group Rockwell International 12214 Lakewood Boulevard Downey CA 90241

In deep space surveillance missions using high-sensitivity infrared sensor systems, satellite targets must be detected among a background scene of thousands of stars. Data stream processing must provide early rejection in the processing stages of the star scene to permit target detection and to meet the downlink data transmission capacity constraint. Two star-elimination techniques are formulated - a multicolor elimination concept, and a moving target indicator (MTI) technique - and their performance analyzed. Multicolor elimination methods reduce the star scene to a star leakage rate of about 39 stars/deg2; the MTI technique reduces the star scene to a dramatically low star leakage rate - to <0.05 stars/deg2.

关键词： Aerospace control Sensor arrays Layout Equations infrared sensors Satellites Object detection Space technology Temperature sensors Performance analysis

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Separate estimation schemes for time-frequency overlapped micro-doppler signal in single channel infrared laser detection

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infrared PHYSICS & technology 2018年 94卷 134-141页

作者： Guo, Liren Hu, Yihua Xu, Shilong Dong, Xiao Li, Minle Natl Univ Def Technol Coll Elect Engn State Key Lab Pulsed Power Laser Technol Hefei 230037 Anhui Peoples R China Anhui Prov Key Lab Elect Restrict Technol Hefei 230037 Anhui Peoples R China

The micro-Doppler (MD) effect of weak vibration target is obvious in infrared laser detection. This provides the foundation for precise estimation of micro-motion parameters, and makes the target classification and recognition possible. The multi-targets or multi-scattering points existing in the detecting field will generate the single-channel multi-component (SCMC) signal in laser detection. Further, the similar micro-motion parameters will lead to the feature overlapping in time-frequency domain, which will increase the difficulty of parameter estimation. In this paper, a separate parameter estimator based on the maximum likelihood framework and singular value decomposition is proposed to deal with this mixed signal. First, an improved singular value ratio (SVR) spectrum with detailed period scanning is presented to locate the vibration frequency. The amplitude ratio information of each component is also extracted from the SVR spectrum. Then, the analytic expression of the maximum likelihood estimation (MLE) of micro-motion parameters is derived. To solve the high nonlinear\ problem in laser MD signal, a new likelihood function (LF) is designed in the derivation process. The Robustness and efficiency are both increased with this new LF. The Markov chain Monte Carlo (MCMC) sampling is employed to implement the MLE. Finally, the simulation results verifies the validity of the proposed method. The comparison with the Cramer-Rao bound shows the ability of accurate estimation of the proposed method.

关键词： Laser micro-Doppler Parameter estimation Maximum likelihood estimation Singular value ratio spectrum

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Automatic target recognition and detection in infrared imagery under cluttered background 3

Automatic target recognition and detection in infrared image...

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Congress on target and Background Signatures III

作者： Gundogdu, Erhan Koc, Aykut Alatan, A. Aydin ASELSAN Res Ctr Intelligent Data Analyt Res Prog Dept Ankara Turkey Middle East Tech Univ Elect & Elect Engn Dept Ankara Turkey Middle East Tech Univ Ctr Image Anal OGAM Ankara Turkey

ISBN: (纸本)9781510613294;9781510613287

Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

关键词： autoencoders neural networks infrared object recognition

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Performance of diffuse reflectance infrared Fourier transform spectroscopy and chemometrics for detection of multiple adulterants in roasted and ground coffee

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LWT-FOOD SCIENCE AND technology 2013年第2期53卷 395-401页

作者： Reis, Nadia Franca, Adriana S. Oliveira, Leandro S. Univ Fed Minas Gerais PPGCA BR-31270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais DEMEC BR-31270901 Belo Horizonte MG Brazil

The quality of roasted and ground coffee is an important issue since it has been the target of fraudulent admixtures with a variety of cheaper materials, including spent coffee grounds, coffee husks and other roasted grains. Given the successful application of Diffuse Reflectance infrared Fourier Transform Spectroscopy (DRIFTS) for discrimination between roasted coffee, corn and coffee husks, the objective of this work was to confirm the potential of such technique for discrimination between pure roasted coffee and coffee samples adulterated with coffee husks, corn, barley and spent coffee grounds, regardless of roasting conditions. Principal Components Analysis (PCA) was employed for selection of target spectra regions responsible for group discrimination. classification models were developed based on Linear Discriminant Analysis (LDA) and recognition and prediction abilities of these models were 100%, with the samples being separated into six groups: pure coffee, adulterated coffee (adulteration levels as low as 1 g/100 g), spent coffee grounds, coffee husks, corn and barley. Such results confirm that DRIFTS can be a valuable analytical tool for detection of adulteration in ground and roasted coffee. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Barley Corn Coffee adulteration Coffee husks Spent coffee grounds

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Imaging detection and classification for Near-space Dim targets 7

Imaging Detection and Classification for Near-space Dim Targ...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of Engineering & technology 2023.

关键词： Extraction

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Micro Solder Defect Inspection Using infrared Sequence and Deep Learning Learning Omni-scale CNN 24

Micro Solder Defect Inspection Using Infrared Sequence and D...

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24th International Conference on Electronic Packaging technology (ICEPT)

作者： Jiang, Ye Liu, Zhiyong Liao, Guanglan Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798350338812;9798350338829

The chip on PCBA is developing towards miniaturization and high density. It becomes more difficult to detect micro solder bump defects inside the electronic package and soldering defects cause 71% of electronic package problems. However, fault diagnosis in the industrial manufacturing scenario is more complex and diverse involving the detection and location of solder bump defects at the IC level. As an attractive option to package inspection, X-ray may seriously affect the service life of ICs. Our group proposed a nondestructive diagnosis method on the basis of active thermography. In the recorded thermal image, the solder ball was segmented for the classification analysis. Due to the large size of PCBA, the narrow gap laser is limited to industrial applications. Because of the inefficiency and low identification rate of current detection, here we propose a detection method using infrared thermal imaging and Omni-scale CNN deep learning algorithm. We take the real chip as the experimental target. The infrared thermal sequence is collected to analyze the temperature evolution of different defect types. We use the target region segmentation to extract features to distinguish the invisible solder defects in infrared images. With the hyperparameter optimization, the Omni-scale CNN classification model realizes efficient and highly accurate detection of different solder (400 mu m-500 mu m) defect types.

关键词： Solder bump infrared sequence Chip on board Defect detection Deep learning

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Hybrid system for automatic target recognition

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Proceedings of SPIE - The International Society for Optical Engineering 1994年 2269卷 280-291页

作者： Eustache, Erik Gingras, Denis J. Poussart, Denis Natl. Optics Inst. St.e-Foy PQ Can

ISBN: (纸本)0819415936;9780819415936

A hybrid system is proposed for automatic target recognition. The system developed consists of an optical module and an electronic/computer module. The electronic module uses a DatacubeTM image processing system to carry out real-time filtering, detection, and segmentation operations. The optical module is composed of two LCTVs (liquid crystal television), a He-Ne laser, a collimator, and a CCD camera. This module calculates specific energy spectra in the Fourier plane of a potential target identified by the electronic module. These values are fed in a neural network implemented on Sun SPARC 10TM model 41 for the classification of the current target. Experimental results involving special classes of target and their distribution in the features space are presented.

关键词： Pattern recognition

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ACDF-YOLO: Attentive and Cross-Differential Fusion Network for Multimodal Remote Sensing Object detection

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REMOTE SENSING 2024年第18期16卷 3532页

作者： Fei, Xuan Guo, Mengyao Li, Yan Yu, Renping Sun, Le Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China

Object detection in remote sensing images has received significant attention for a wide range of applications. However, traditional unimodal remote sensing images, whether based on visible light or infrared, have limitations that cannot be ignored. Visible light images are susceptible to ambient lighting conditions, and their detection accuracy can be greatly reduced. infrared images often lack rich texture information, resulting in a high false-detection rate during target identification and classification. To address these challenges, we propose a novel multimodal fusion network detection model, named ACDF-YOLO, basedon the lightweight and efficient YOLOv5 structure, which aims to amalgamate synergistic data from both visible and infrared imagery, thereby enhancing the efficiency of target identification in remote sensing imagery. Firstly, a novel efficient shuffle attention module is designed to assist in extracting the features of various modalities. Secondly, deeper multimodal information fusion is achieved by introducing a new cross-modal difference module to fuse the features that have been acquired. Finally, we combine the two modules mentioned above in an effective manner to achieve ACDF. The ACDF not only enhances the characterization ability for the fused features but also further refines the capture and reinforcement of important channel features. Experimental validation was performed using several publicly available multimodal real-world and remote sensing datasets. Compared with other advanced unimodal and multimodal methods, ACDF-YOLO separately achieved a 95.87% and 78.10% mAP0.5 on the LLVIP and VEDAI datasets, demonstrating that the deep fusion of different modal information can effectively improve the accuracy of object detection.

关键词： multimodal fusion attention cross-modal difference module remote sensing images object detection

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Real-time thermal infrared moving target detection and recognition using deep learned features 30

Real-time thermal infrared moving target detection and recog...

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Conference on Automatic target Recognition XXX

作者： Akula, Aparna Kaur, Varinder Guleria, Neeraj Ghosh, Ripul Kumar, Satish CSIR Cent Sci Instruments Org India New Delhi India

ISBN: (数字)9781510635661

ISBN: (纸本)9781510635661;9781510635654

Surveillance applications demand round the clock monitoring of regions in constrained illumination conditions. Thermal infrared cameras which capture the heat emitted by the objects present in the scene appear as a suitable sensor technology for such applications. However, developing of AI techniques for automatic detection of targets for monitoring applications is challenging due to high variability of targets within a class, variations in pose of targets, widely varying environmental conditions, etc. This paper presents a real-time framework to detect and classify targets in a forest landscape. The system comprises of two main stages: the moving target detection and detected target classification. For the first stage, Mixture of Gaussians (MoG) background subtraction is used for detection of Region of Interest (ROI) from individual frames of the IR video sequence. For the second stage, a pre-trained Deep Convolutional Neural Network with additional custom layers has been used for the feature extraction and classification. A challenging thermal dataset created by using both experimentally generated thermal infrared images and from publically available FLIR Thermal Dataset. This dataset is used for training and validating the proposed deep learning framework. The model demonstrated a preliminary testing accuracy of 95%. The real-time deployment of the framework is done on embedded platform having an 8-core ARM v8.2 64-bit CPU and 512-core Volta GPU with Tensor Cores. The moving target detection and recognition framework achieved a frame rate of approximately 23 fps on this embedded computing platform, making it suitable for deployment in resource constrained environments.

关键词： deep neural network infrared imaging transfer learning classification wildlife monitoring poaching

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