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检索条件"任意字段=Infrared Technology for Target Detection and Classification 1981"

共 301 条记录，以下是101-110 订阅

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Small infrared Aerial target detection Using Spatial and Temporal Cues 11th

Small Infrared Aerial Target Detection Using Spatial and Tem...

引用

11th International Conference on Image and Graphics, ICIG 2021

作者： Guo, Liangchao Zhang, Wenlong Sun, Xiaoliang Wang, Zi Shang, Yang National University of Defense Technology Changsha China

ISBN: (纸本)9783030873547

Due to rapidly relative motion between target and infrared imaging platform, clutter background, etc., robust small infrared aerial target detection is still an open problem. A novel small infrared aerial target detection method using spatial and temporal cues is proposed in this paper. First, using spatial cues, we take target candidate detection as a binary classification problem. target candidates in each single frame are detected via interesting pixel detection and a trained LightGBM model. Then, using temporal cues, we model the local smoothness and global continuous characteristic of the target trajectory as short-strict and long-loose constraints. The trajectory constraints within image sequence are used in detecting the true small infrared aerial targets from numerical target candidates. Experiment results on the public dataset SIATD show that the proposed method performs better than other existing methods in detecting small infrared aerial target and shows great robustness toward clutter backgrounds. © 2021, Springer Nature Switzerland AG.

关键词： Antennas

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Development of bicycle and pedestrian detection and classification algorithm for active-infrared overhead vehicle imaging sensors

Development of bicycle and pedestrian detection and classifi...

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作者： Noyce, David A. Gajendran, Arunkumar Dharmaraju, Raghuram Department of Civil and Environmental Engineering University of Wisconsin-Madison 1210 Engineering Hall 1415 Engineering Drive Madison WI 53706 United States University of Massachusetts-Amherst Department of Civil and Environmental Engineering 139 Marston Hall Amherst MA 01003 United States TJKM Transportation Consultants 5960 Inglewood Drive Pleasanton CA 94588 United States Vermont Agency of Transportation 1 National Life Drive Drawer 33 Montpelier VT 05633 United States

ISBN: (纸本)0030099927

Existing algorithms used with active-infrared overhead vehicle-imaging sensors consider vehicle size and speed attributes as basic parameters to detect and classify 11 categories of motorized vehicles. These algorithms could not detect and classify bicycles and pedestrians. This research focused on developing and evaluating algorithms for active-infrared overhead vehicle-imaging sensor technology to detect and classify non-motorized users. Development of the theory and algorithm used to automate the simultaneous detection and classification of bicycles and pedestrians along with the field investigations to evaluate its effectiveness are described. The new algorithm used the concept of message sequencing to incorporate existing active-infrared technology theory. Bicycles and pedestrians intersected the infrared scan patterns in different sequences that, along with the infrared images, provided unique detection and classification identification. The algorithm was integrated within the existing active-infrared technology, and a field evaluation was conducted on bicycle and pedestrian trails. The algorithm created an intelligent technology to detect and classify bicycles and pedestrians. Nearly 100% of bicycles and pedestrians were detected, and about 92% of them were successfully classified. Automated data collection technology can be useful in obtaining more comprehensive travel data and in forecasting demand for design and policy making related to nonmotorized transportation.

关键词： Image sensors

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Applying self-structured data learning algorithm to aerial infrared and visual images

Applying self-structured data learning algorithm to aerial i...

引用

Cyber Sensing 2017

作者： Li, Jenfeng Sam Ternovskiy, Igor Graham, James Ilin, Roman Georgia Institute of Technology AtlantaGA30332 United States Automatic Target Recognition Center Right State Research Institute BeavercreekOH45431 United States Sensors Directorate Air Force Research Laboratory Wright Patterson AFB OH45433 United States Riverside Research BeavercreekOH45431 United States

ISBN: (纸本)9781510608719

Previously, we proposed and implemented a Self-structuring Data Learning Algorithm. This realized software package and the concept are still progressing. Earlier, it was tested with synthetic data and exhibited interesting results. The objectives of this paper are testing the algorithm with raw infrared and visual images and updating the algorithm as required. We first performed registration transformation and detection from the images with an existing software package. We then registered the detections with the registration transformations from both infrared and visual images. The registered detections were delivered to the algorithm for target detection and tracking without modification. Results revealed inability to handle very noisy infrared image features. To overcome this problem, we developed multiscale grid processing to improve detection classification in the algorithm. This updated algorithm shows much better target detection and tracking with the real-world data. More algorithm enhancements are in work such as incorporating pattern recognition, classification, and fusion. © 2017 SPIE.

关键词： Learning algorithms

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Feature long axis size and local luminance contrast determine ship target acquisition performance: strong evidence for the TOD case 13

Feature long axis size and local luminance contrast determin...

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Conference on Electro-Optical and infrared Systems - technology and Applications XIII

作者： Bijl, Piet Toet, Alexander Kooi, Frank L. TNO Kampweg 5 NL-3769 DE Soesterberg Netherlands

ISBN: (数字)9781510603790

ISBN: (纸本)9781510603783;9781510603790

Visual images of a civilian target ship on a sea background were produced using a CAD model. The total set consisted of 264 images and included 3 different color schemes, 2 ship viewing aspects, 5 sun illumination conditions, 2 sea reflection values, 2 ship positions with respect to the horizon and 3 values of atmospheric contrast reduction. In a perception experiment, the images were presented on a display in a long darkened corridor. Observers were asked to indicate the range at which they were able to detect the ship and classify the following 5 ship elements: accommodation, funnel, hull, mast, and hat above the bridge. This resulted in a total of 1584 target Acquisition (TA) range estimates for two observers. Next, the ship contour, ship elements and corresponding TA ranges were analyzed applying several feature size and contrast measures. Most data coincide on a contrast versus angular size plot using (1) the long axis as characteristic ship/ship feature size and (2) local Weber contrast as characteristic ship/ship feature contrast. Finally, the data were compared with a variety of visual performance functions assumed to be representative for target Acquisition: the TOD (Triangle Orientation Discrimination), MRC (Minimum Resolvable Contrast), CTF (Contrast Threshold Function), TTP (targeting Task Performance) metric and circular disc detection data for the unaided eye (Blackwell). The results provide strong evidence for the TOD case: both position and slope of the TOD curve match the ship detection and classification data without any free parameter. In contrast, the MRC and CTF are too steep, the TTP and disc detection curves are too shallowand all these curves need an overall scaling factor in order to coincide with the ship and ship feature recognition data.

关键词： target acquisition ship detection ship classification TOD MRC TTP CTF Johnson criterion

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An information theoretic model of target discrimination using hyperspectral and multisensor data

An information theoretic model of target discrimination usin...

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34th Conference on infrared technology and Applications

作者： Wadstromer, Niclas Renhorn, Ingmar Swedish Def Res Agcy FOI Div Sensor Syst SE-58111 Linkoping Sweden

ISBN: (纸本)9780819471314

We address the problem of target discrimination using hyperspektral and multisensor data. The problem is of significance to detection and classification of low signature targets such as landmines. The problem will be described with stochastic models and by using information theoretic concepts we will derive limits to system performance in terms of probability of false alarm and probablility of detection. The stochastic model is suitable to evaluate and optimize sensor parameters and sensor configurations. We will for example investigate how much information different combinations of spectral and spatial data will give. With the stochastic model sensors with different types of characteristics can be compared and the contribution of the different sensors and configurations can be evaluated. Besides the optimization of different sensor configurations with respect to specific applications, the stochastic model will be used to evaluate different anomaly detectors. The strength of our approach is shown by examples from an analysis of measurements on a natural scene with various objects using an electro-optical hyperspectral sensor and several other sensors. We expect that our approach will give significant indications on how to choose and configure sensors for efficient and reliable target discrimination.

关键词： target discrimination information theory hyperspectral data multisensor data

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Design and tuning of FPGA implementations of neural networks

Design and tuning of FPGA implementations of neural networks

引用

Conference on Automatic target Recognition VII

作者： Clare, PJC Gulley, JW Hickman, D Smith, MI Central Research Labs. Ltd. (United Kingdom) Pilkington Optronics (United Kingdom)

ISBN: (纸本)0819424846

Artificial Neural Network (ANN) algorithms are applicable in a variety of roles for image processing in infrared Search and Track (IRST) systems. Achieving a high throughput is a key objective in developing ANNs for processing large numbers of pixels at high frame rates. Previous work has investigated the use of a neural core supported by configurable logic to achieve a versatile technology applicable to a variety of systems. The implementation of Multi-Layer Perceptron (MLP) ANNs, using Field Programmable Gate Array (FPGA) technology to ensure upgradability and reconfigurability, is the focus of this research. Approximations to the MLP algorithms are needed to ensure that a high throughput can be achieved with a sufficiently low gate count.

关键词： infrared search and track neural networks multi-layer perceptrons throughput gate count pixel processing field programmable gate array target detection target classification

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Classifying Objects in LWIR Imagery via CNNs 13

Classifying Objects in LWIR Imagery via CNNs

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Conference on Electro-Optical and infrared Systems - technology and Applications XIII

作者： Rodger, Iain Connor, Barry Robertson, Neil M. Heriot Watt Univ Sch Engn & Phys Sci Riccarton EH14 4AS Midlothian Scotland Thales UK 1 Linthouse Rd Glasgow G51 4BZ Lanark Scotland Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

ISBN: (数字)9781510603790

ISBN: (纸本)9781510603783;9781510603790

The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

关键词： LongWave Thermal-infrared Object classification Convolutional Neural Network Machine Learning

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infrared Image target detection Algorithm for Hardware Fittings at Tension Clamp of Qishao UHV Transmission Line 4

Infrared Image Target Detection Algorithm for Hardware Fitti...

引用

4th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Zhang, Wei Dong, Weiguang Li, Shengchang Lanzhou Jiaotong University School of Automation and Electrical Engineering Lanzhou China

ISBN: (纸本)9781665463997

The infrared thermal imager is a common method for electric inspection to detect the abnormal heat of the gold tool at the tensioning wire clamp. Because the Gansu section of Qishao High voltage line accounts for about 52.4% of the total length, through a lot of complex geographical environment, even once a quarter manual inspection will cost time and effort, subjectivity is big, and real-time performance is poor. Aiming at the above, this paper proposes an improved YOLOv4 multi-target recognition algorithm for infrared images. Firstly, the backbone feature extraction network CSPDaeknet53 is replaced by Mobilenetv1. Secondly, the training dataset is increased by the base hjs image expansion technology. Finally, Mosaic data enhancement and cosine annealing attenuation technology are introduced to construct an effective infrared image detection model to realize the classification and recognition of the hardware. The experimental results are compared with SSD, YOLOv3 and YOLOv4 target detection algorithms, and it is found that the average recognition accuracy of the proposed method is 93.84%, and the model size is only 55.53MB. Under the condition of ensuring accuracy, the model is smaller and has practical reference value. © 2022 IEEE.

关键词： infrared imaging

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Deep Learning-Based Algorithm for Local Feature Recognition of target infrared Polarization 3

Deep Learning-Based Algorithm for Local Feature Recognition ...

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3rd International Conference on Optics and Image Processing, ICOIP 2023

作者： Zou, Zhihui Yang, Shuai Li, Yingchao Shi, Haodong Fu, Qiang School of Optoelectronic Engineering Changchun University of Science and Technology Jilin Changchun China Jilin Provincial Key Laboratory of Space Optoelectronics Technology Jilin Changchun China

ISBN: (纸本)9781510667426

In response to the traditional detection methods of not detecting the target, not seeing it, blurring the outline and details of the image, etc., using a combination of infrared and polarization detection, the infrared image information and the polarization image information are decoded to solve the problem of not being detected or seen in various environments. For the target local feature extraction process of large amounts of data, slow extraction speed, and other problems, an improved SIFT algorithm for local feature extraction of polarized images with deep learning is proposed. Experimental results show that the modified algorithm combines the advantages of polarimetric imaging and deep learning to achieve fast feature extraction of targets in simple or complex backgrounds. The algorithm improves the speed of local feature extraction of polarized images by 3.6% and the accuracy of extraction by 2.8%. The algorithm provides the theoretical basis for target classification, identification, and tracking techniques. © 2023 SPIE. All rights reserved.

关键词： Feature extraction

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A Photogrammetric Approach for Real-time 3D Localization and Tracking of Pedestrians in Monocular infrared Imagery 10

A Photogrammetric Approach for Real-time 3D Localization and...

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Conference on Optics and Photonics for Counterterrorism, Crime Fighting, and Defence X and Optical Materials and Biomaterials in Security and Defence Systems technology XI

作者： Kundegorski, Mikolaj E. Breckon, Toby P. Univ Durham Sch Engn & Comp Sci Durham DH1 3HP England

ISBN: (纸本)9781628413168

target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with actual 3D scene position of a given tracked object in relation to its observed position in 2D image space. We propose an approach that challenges the current trend in complex tracking solutions by addressing this fundamental ambiguity head-on. In contrast to prior work in the field, we leverage the key advantages of thermal-band infrared (IR) imagery for the pedestrian localization to show that robust localization and foreground target separation, afforded via such imagery, facilities accurate 3D position estimation to within the error bounds of conventional Global Position System (GPS) positioning. This work investigates the accuracy of classical photogrammetry, within the context of current target detection and classification techniques, as a means of recovering the true 3D position of pedestrian targets within the scene. Based on photogrammetric estimation of target position, we then illustrate the efficiency of regular Kalman filter based tracking operating on actual 3D pedestrian scene trajectories. We present both a statistical and experimental analysis of the associated errors of this approach in addition to real-time 3D pedestrian tracking using monocular infrared (IR) imagery from a thermal-band camera.

关键词： thermal target tracking temporal filtering intelligent target reporting thermal imaging pedestrian detection people detection sensor networks temporal fusion passive target positioning 3D pedestrian localization

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