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infrared technology for target detection and classification 1981

作者： Casasent, David Pauly, John Fetterly, Donald Carnegie-Mellon University Department of Electrical Engineering PittsburghPA15213 United States General Dynamics Pomona Division PomonaCA91769 United States

An analysis of the statistics of the moments and the conventional invariant moments shows that the variance of the latter become quite large as the order of the moments and the degree of invariance increases. Moreso, the need to whiten the error volume increases with the order and degree, but so does the computational load associated with computing the whitening operator. We thus advance a new estimation approach to the use of moments in pattern recognition that overcomes these problems. This work is supported by experimental verification and demonstration on an infrared ship pattern recognition problem. The computational load associated with our new algorithm is also shown to be very low. © 1982 SPIE. All rights reserved.

关键词： Ships

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基于两阶段时空加权特征的红外目标跟踪算法

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红外技术 2025年第4期47卷 437-444页

作者：李清忠南昌理工学院计算机信息工程学院江西南昌330044

为了有效解决了遮挡、运动模糊、拖尾等干扰影响下跟踪漂移的问题,本文提出一种基于两阶段时空加权特征的红外目标跟踪算法,该算法将目标区域分割成相同尺寸的非重叠区域,并根据相距目标中心的位置信息分配不同的权值,以此推导出具有自... 详细信息

为了有效解决了遮挡、运动模糊、拖尾等干扰影响下跟踪漂移的问题,本文提出一种基于两阶段时空加权特征的红外目标跟踪算法,该算法将目标区域分割成相同尺寸的非重叠区域,并根据相距目标中心的位置信息分配不同的权值,以此推导出具有自适应时空加权贝叶斯分类器;然后,利用改进的度量准则找出具有最大类差的分类样本,具有较高的跟踪适应性,且在目标被遮挡时具备对目标的重捕和跟踪。仿真实验表明,相比SiamFC等主流跟踪算法,所提算法在LSOTB-TIR目标跟踪数据集中重叠率和中心误差指标上均实现显著优化,大幅提升了跟踪稳定性与定位精度,且跟踪速度达到56帧/s,适合工程应用。

关键词：红外图像目标跟踪压缩感知空时加权遮挡检测贝叶斯分析

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infrared Small UAV target detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks

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IEEE ACCESS 2022年 10卷 115988-115995页

作者： Zheng Lu Peng Yueping Ye Zecong Jiang Rongqi Zhou Tongtong Engn Univ PAP Xian 710086 Peoples R China Engn Univ PAP Coll Informat Engn Xian 710086 Peoples R China

Under the explosive growth of "low-slow-small" unmanned aerial vehicles (UAVs), harassment incidents with "low-slow-small" UAVs, such as illegal surveying and mapping, no-fly zone flights, terrorist attacks, have frequently been occurring due to the low entry barrier to the industry and the lack of market norms and control standards, and severely impair the safety of public life and property. The premise of developing anti-UAV technology is efficient and accurate UAV target detection, which plays a vital role in the follow-up UAV target tracking, attack, and interception. This paper proposes a new DEAX algorithm named enhanced adaptive feature pyramid networks-based target detection Algorithm for infrared small UAV target detection. Our proposed algorithm improves the original feature pyramid networks in four aspects for the small UAV target detection task. 1) Use channel separation to keep more channels when adjusting the number of channels in convolutional to avoid the loss of helpful feature information;2) Design a feature enhancement module to enhance "target feature" and suppress "non-target feature";3) Alleviate the differences of receptive fields and semantic information among different layers by shared convolution;4) Introduce adaptive feature fusion method into feature fusion and complete the construction of enhanced adaptive feature pyramid networks (EAFPN) to solve the problem of weakening feature expression in cross-scale feature fusion. EAFPN is applied to the single-stage target detection networks. We find the detection accuracy and speed of the algorithm outperform those based on feature pyramid networks (FPN), where the improvement on mAP is 8.9%.

关键词： Feature extraction Object detection Semantics detection algorithms target tracking Imaging classification algorithms Object detection feature pyramid feature enhancement

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Feature analysis for forward looking infrared (FLIR) target identification

Feature analysis for forward looking infrared (FLIR) target ...

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infrared technology for target detection and classification 1981

作者： Lin, Yun-Kung J. Government Communications Systems Division Harris Corporation Mail Stop 12-320 P.O. Box 37 MelbourneFL32901 United States

Feature analysis and classifier design toward real-time FLIR target identification are presented. After tarqet candidates of military vehicles are segmented in a low resolution FLIR scenario,1 a set of 17 features is extracted from these target areas in a single pass. These features consist of two optical intensity features, nine geometry features as well as six texture features. Five types of target candidates are examined in this study. They are tanks, APC's, jeeps, burning hulks, and other nontargets such as noise regions. A simple tree classifier is designed that is based on the manual interpretation of feature distribution among target categories at each nonterminal node of the tree classifier. The approach of feature extraction and classifier design has been applied to FLIR imagery provided by the U.S. Army Night Vision Laboratory and has shown promising results. © 1982 SPIE. All rights reserved.

关键词： Military vehicles

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Optimal performance limits for detection and classification algorithms

Optimal performance limits for detection and classification ...

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infrared technology for target detection and classification 1981

作者： Barlow, Carl A. Stern, Martin New Products Development Department Texas Instruments Incorporated Mail Station 3407 P.O. Box 405 LewisvilleTX75067 United States

A system which accepts a two-dimensional scene as input and produces an image of the scene as output introduces degradations to the image which cause a loss of information about the original scene. A mathematical model is described which, under certain assumptions about the scene, the transformations and the noise sources, models the effects of these degradations on the original scene and on the ability of any algorithm to classify objects in the scene. Three possible applications of this theory are (1) establishing whether a classification problem is supported by sufficient sensor data to admit a solution (2) determining which noise or system functions are most limiting and (3) providing an upper-bound performance against which suboptimal algorithms may be compared so as to establish the potential benefit which could result from algorithm improvements. © 1982 SPIE. All rights reserved.

关键词： Mathematical transformations

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target recognition, identification and tracking using real and synthetic IR imagery

Target recognition, identification and tracking using real a...

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22nd infrared technology and Applications Conference

作者： Pentecost, HTA Rogoyski, AM McCann, DR Maurice, AJ DEF RES AGCY SEVENOAKS TN14 7BPKENTENGLAND

ISBN: (纸本)0819421251

In the development of automatic target detection (ATD) and automatic target recognition/identification (ATR/I) systems, the issue of image data is commonly given inadequate consideration. All too often a poorly managed collection of real data, or unrealistic synthetic data is used during development, resulting in a loss of performance when used in the field with imagery having different characteristics. Some of the most promising approaches to ATD and ATR/I, such as neural networks, are particularly susceptible to this problem due to their direct dependence on the training data. This paper highlights the issues involved, with reference to a generic detection and classification approach and to the use of real and synthetic infra-red imagery at the Defence Research Agency at Fort Halstead in the United Kingdom.

关键词： Image segmentation Neural networks Image classification target recognition Feature extraction Image processing target detection Data acquisition Defense and security infrared imaging

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结合Swin Transformer与MobileNetv3的多源无人机影像目标检测方法

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城市勘测 2025年第1期 27-32页

作者：王新广李辉天津市勘察设计院集团有限公司天津300191 星际空间(天津)科技发展有限公司天津300384

针对无人机载荷硬件算力有限,而当前轻量级模型对无人机影像内目标检测精度不佳的问题,设计了一种基于MobileNetv3的轻量级目标检测模型。在特征提取层内引入通道混排注意力机制,同步捕捉通道与空间维度的注意力特征,同时在网络末端引... 详细信息

针对无人机载荷硬件算力有限,而当前轻量级模型对无人机影像内目标检测精度不佳的问题,设计了一种基于MobileNetv3的轻量级目标检测模型。在特征提取层内引入通道混排注意力机制,同步捕捉通道与空间维度的注意力特征,同时在网络末端引入移动式窗口式视觉变压器模块,计算全局尺度的上下文语义特征;通过双向加权特征金字塔实现多尺度特征加权融合,并在其中引入深度可分离卷积核与动态上采样层,降低融合阶段计算消耗;参考YOLOv7损失函数结构,采用焦点-高效交并比函数计算目标框回归损失,采用梯度协调函数计算目标分类损失。在光学及热红外影像数据集上的实验结果表明,所提模型较原模型在检测速度方面提升了3.52%与3.83%,同时mAP 0.5也分别提高7.11%与6.85%,与对照组内主流轻量级检测模型相比,本文模型在检测精度、速度及体量方面,具有一定的优势,适合部署在无人机载荷硬件中,针对复杂地面场景,开展全天候目标实时检测。

关键词：多源无人机影像轻量级目标检测 MobileNetv3 移动式窗口视觉变压器动态上采样

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infrared Image Recognition technology Based on Visual Processing and Deep Learning

Infrared Image Recognition Technology Based on Visual Proces...

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Chinese Automation Congress (CAC)

作者： He Feng Hu Xuran Liu Bin Wang Haipeng Zhang Decai Shandong Elect Power Co Jinan Power Supply Co Jinan 250101 Peoples R China State Grid Intelligence Technol Co Ltd Jinan 250101 Peoples R China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176871

infrared image recognition in substation is always a difficult problem. In order to solve the problem of recognition of knife gate, insulator and other components in infrared image, the target location technology of deep learning is proposed to realize the detection and recognition of typical components in infrared image, and the multi-target detection algorithm YOLO is selected to locate and identify the defects. Firstly, the image preprocessing technology is used to process the collected image, so as to filter the interference of background and other factors on the equipment identification. Then, the infrared image is detected by the YOLO target detection model based on multi feature fusion, so as to locate the position of inspection equipment in the infrared image. Then, the type of equipment is identified by the trained equipment classification model. Finally, the algorithm is tested with a large number of pictures in the substation scene.

关键词： infrared image recognition substation scene classification model

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infrared and small target detection based on convolution neural network under the ground-air background 7

Infrared and small target detection based on convolution neu...

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7th Symposium on Novel Photoelectronic detection technology and Applications

作者： Wan, Yaohui Chen, Liangyu Liu, Yingchao Key Lab Opt Radiat Beijing 100039 Peoples R China

ISBN: (数字)9781510643628

ISBN: (纸本)9781510643628

In this paper, a detection method of infrared dim target under ground-sky background is proposed. In view of the situation that there are more edges under the ground-sky background, the top-hat and median filtering algorithm are used for preliminary processing, and a large number of boundaries are estimated by combining Laplacian operator and Canny operator, and then the difference is made to eliminate the influence of boundary on detection, leaving some suspected target points. Finally, a lightweight convolution neural network is used to determine the suspected target points, and the regression problem of the target position is simplified to a binary classification problem. The experimental results show that the proposed algorithm has higher detection probability and lower false alarm rate than the traditional infrared small target detection algorithm under the ground-sky background, and has good detection effect.

关键词： ground-sky background infrared small target convolution neural network Canny Laplacian top-hat

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Comparison of imaging infrared detection algorithms

Comparison of imaging infrared detection algorithms

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infrared technology for target detection and classification 1981

作者： Burton, Mark Benning, Carl Image Processing Systems Department Equipment Group Texas Instruments Incorporated M.S. 3407 P.O. Box 405 LewisvilleTX75067 United States

This paper compares the performance of four candidate target detection algorithms. The best known of these, "Superslice," was developed at the University of Maryland in 1977-78.1 The other three algorithms are the "Spoke Filter" developed by the Army Missile Command in Huntsville;2,3 the "contrast box" (CB), a new concept under development at Texas Instruments;and the Ford-Aerospace double-gated contrast filter.4 As part of Texas Instruments development activity, CB algorithm performance was compared with the others. To do this meaningfully, all four concepts were tested using a common data base. The measure of detection performance was the total number of candidate targets handed off to the feature extractor and classifier to achieve a specified probability of including the actual target in the total. An ideal detection algorithm in terms of this measure would only need to hand off one target (assuming only one target in the FOV) to achieve a probability of 1. Other performance measures were input and output signal-to-noise ratios (SNR) and algorithm gain. The data base used in this investigation consisted of 256 independent IR images containing targets nominally varying between 5 and 500 pixels in size. The results presented not only illustrate how the algorithm performance depends on range to the target, but show that no one algorithm is best for all values of pixels on target. Of the four, however, the contrast box appears to provide the best overall performance. © 1982 SPIE. All rights reserved.

关键词： Pixels

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