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Techniques and Applications of Image Understanding.

作者： Graziano, James M. Gerhart, Grant R. Hannigan, Joseph F.

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FPGA Implementation of a Hardware Optimized Automatic target detection and classification Algorithm for Hyperspectral Image Analysis

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Macias, Ruben Bernabe, Sergio Bascones, Daniel Gonzalez, Carlos Univ Complutense Madrid Comp Arquitecture & Automat Madrid 28040 Spain

In hyperspectral image (HSI) analysis, one of the most important tasks is target detection, requiring the execution of algorithms with high computational complexity. Recently, research efforts have focused on on-board real-time target detection to provide timely responses for swift decisions. Therefore, it is necessary to use a technology that provides the performance needed for real-time target detection, and at the same time meets the satellite payload requirements. Field-programmable gate arrays (FPGAs) have very interesting properties in terms of performance, size, and power consumption, which have become the standard option for on-board processing. In this letter, we present a hardware optimized implementation for FPGAs of the automatic target detection and classification algorithm (ATDCA) using the GramSchmidt (GS) method for orthogonalization purposes. The ATDCA-GS algorithm is directly coded using VHDL and verified on a Virtex-7 XC7VX690T FPGA using real hyperspectral data [collected by Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor and by NASAs Airborne Visible/infrared Imaging Spectrometer (AVIRIS)] and a synthetic image. Experimental results demonstrate that our hardware version of the ATDCA-GS algorithm outperforms previous implementations (multicore processors, GPUs, and accelerators) in both computation time (obtaining real-time performance) and power consumption, demonstrating the suitability of FPGAs for this purpose.

关键词： Field programmable gate arrays Object detection Hardware classification algorithms Hyperspectral imaging Real-time systems Software algorithms Automatic target detection and classification algorithm (ATDCA) field-programmable gate arrays (FPGAs) Gram-Schmidt (GS) orthogonalization hyperspectral imaging reconfigurable hardware

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A knowledge-based approach to the detection, tracking and classification of target formations in infrared image sequences

A knowledge-based approach to the detection, tracking and cl...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： J.F. Bronskill J.S.A. Hepburn W.K. Au Advanced Technology Centre Honeywell Limited Markham ONT Canada Systems and Research Center Honeywell Inc. Minneapolis MN USA

A knowledge-based approach to the detection, tracking, and classification of ground-based formations of point targets in sequences of digitized forward-looking infrared (FLIR) image sequences is presented. The system has two components: a point target detector and tracker (PTD) which processes the image sequences and supplies candidate point targets to the knowledge-based target formation detector for clustering into formations, which are then classified as linear, V, and the like. The system has been implemented in software written in C and Common Lisp and evaluated on a variety of FLIR image sequences. The results indicate that, in all cases, the knowledge base improves system performance over that attained by the PTD alone.< >

关键词： target tracking infrared detectors Image sequences Sensor phenomena and characterization infrared image sensors Radar tracking Object detection Libraries infrared imaging System performance

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基于CBAM改进YOLOv7的电力设备红外图像分类检测

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红外技术 2025年第1期47卷 72-80页

作者：陈佳余成波王士彬蒋启超何鑫张未重庆理工大学电气与电子工程学院重庆400054 重庆市能源互联网工程技术研究中心重庆400054 国网重庆市电力公司市南供电分公司重庆401336

针对复杂环境下电力设备红外图像的深度学习目标检测数据繁杂、检测精度较低等问题,本文提出一种基于卷积块注意模块(Convolutional Block Attention Module,CBAM)改进YOLOv7的电力设备红外图像分类算法。首先将已有数据集进行标注,并... 详细信息

针对复杂环境下电力设备红外图像的深度学习目标检测数据繁杂、检测精度较低等问题,本文提出一种基于卷积块注意模块(Convolutional Block Attention Module,CBAM)改进YOLOv7的电力设备红外图像分类算法。首先将已有数据集进行标注,并按一定比例划分成训练集、验证集以及测试集,然后在YOLOv7的主干网络中引入CBAM,使模型能对感兴趣的区域进行强调并抑制无用信息,其次将划分好的数据集放入改进后的YOLOv7进行模型训练,同时对比了6种改进的YOLOv5s模型。实验结果表明,在相同实验条件下改进YOLOv7模型优于YOLOv7模型、YOLOv5s模型和基于YOLOv5s的6种注意力模型。改进YOLOv7性能有明显提升,可实现快速、精准的红外图像分类。

关键词：电力设备 YOLOv7 红外图像 CBAM

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classification of ISAR Images Generated from Model-based Approaches Using Deep Learning

Classification of ISAR Images Generated from Model-based App...

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International Conference on Information Networking

作者： Soyoon Park Seonmin Cho Seongwook Lee Taewon Jeong Youngjae Choi Seungeui Lee Youngseok Bae School of Electrical and Electronics Engineering College of ICT Engineering Chung-Ang University Seoul Republic of Korea Institute of Radio Frequency Engineering and Electronics Karlsruhe Institute of Technology Karlsruhe Germany Advanced R&D Center Hanwha Systems Gyeonggi-do Republic of Korea Emerging Science and Technology Directorate Agency for Defense Development Daejeon Republic of Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The classification of small, low-observable airborne targets, such as drones and birds, poses significant challenges due to their low detection rates. Conventional vision sensorbased approaches often suffer from reduced performance in low-visibility environments or adverse weather conditions. Additionally, the integration of infrared sensors alongside camera sensors increases hardware complexity and cost, rendering such solutions inefficient. To address these limitations, we propose a method that leverages deep learning and inverse synthetic aperture radar (ISAR) imaging for accurate target classification, using only radar sensors. Our proposed deep learning-based ISAR image classifier comprises two key components: simulated ISAR image generation and deep learning-based classification. We construct simulated ISAR datasets using point scatter (PS) modeling for quadcopter drones, hexacopter drones, and aircraft, and three-dimension (3D) mesh modeling for birds, unmanned aerial vehicles, and quadcopter drones. The two datasets based on PS and 3D mesh modeling are used to train a proposed deep learning classifier. The proposed classifier can achieve a classification accuracy of $\mathbf{9 8 \%}$ on the PS-based dataset and $\mathbf{9 6 \%}$ on the 3D mesh-based dataset, where scattering was calculated using the physical optics method.

关键词： Deep learning Solid modeling Three-dimensional displays Accuracy Atmospheric modeling Physical optics Radar imaging Inverse synthetic aperture radar Drones Quadrotors

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Radar Based target detection and classification

Radar Based Target Detection and Classification

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： G Santhanamari Muni Jyothi Prakash S Dharshini B Kousalya S Akash R Department of ECE PSG Institute of Technology and Applied Research Coimbatore Associate Engineer Qualcomm India Pvt. Ltd Bengaluru Assistant Engineer Delta Electronics Pvt. Ltd Bengaluru System Engineer Societe Generale Global Solution Centre Bengaluru

In recent years, RADARs have begun to replace more sensors like cameras, ultrasonic devices, and infrared sensors used in common places in everyday life. As integrated circuit technology has advanced, the environmental robustness of radar technology has enabled small, inexpensive, short-range radars operating at frequencies ranging from a few GHz to hundreds of GHz (millimeter waves). As a result, RADAR has become a popular choice for many new applications, including patient monitoring, adaptive control of self-driving cars and drones, structure monitoring, airport security, and gesture recognition. Another recent area of interest is RADAR-based wildlife anti- poaching activities. The model's performance is assessed using the spectrogram obtained from a radar signature database of moving targets in a typical setting. The goal of this work is to discriminate between moving human and animal targets based on variation in range of red cells in the spectrogram. Observed results indicate that the model's classification accuracy depends on the length of continuous on target observation. The radar-based target detection and classification system used for border security measures and wildlife anti-poaching operations for animals like rhinos or elephants are both potential activities.

关键词： Patient monitoring Wildlife Radar detection Radar Object detection Millimeter wave radar Robustness

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基于改进YOLOx-nano的海上红外目标检测算法

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红外 2025年第2期46卷 49-56页

作者：张俊位门吕璐中国电子科技集团公司第五十八研究所

提出了一种基于改进YOLOx-nano的海上红外目标检测算法。通过对检测头的分类与定位任务进行解耦，并引入改进的特征金字塔网络(Feature Pyramid Network, FPN)结构，不仅提升了模型的精度和收敛速度，而且提高了红外大目标检测能力。将... 详细信息

提出了一种基于改进YOLOx-nano的海上红外目标检测算法。通过对检测头的分类与定位任务进行解耦，并引入改进的特征金字塔网络(Feature Pyramid Network, FPN)结构，不仅提升了模型的精度和收敛速度，而且提高了红外大目标检测能力。将改进的压缩和激励网络(Squeeze-and-Excitation Network, SENet)通道注意力机制模块加入到模型中，增强了模型的非线性表达能力，同时提高了有效特征学习能力。为了加快嵌入式平台模型的前向推理速度，引入剪枝技术来实现模型剪枝，在保证召回率不降低的情况下减少模型参数。通过测试集对本文算法进行了验证。结果表明，该算法的平均精度(Average Precision, AP)比原始YOLOx-nano算法提高了1.35%,达到了93.92%。本文算法平衡了模型精度与耗时的矛盾关系，在提升性能的同时，保证了模型检测的速度。

关键词：红外目标检测 YOLOx-nano 模型剪枝注意力机制

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基于红外与可见光图像匹配的巡检机器人障碍识别与分类

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红外技术 2025年第1期47卷 81-88页

作者：邱刚张廼龙柏仓谭笑陈杰高嵩国网江苏省电力有限公司电力科学研究院江苏南京211103 国网江苏省电力有限公司江苏南京210000

在电网、桥梁等较为复杂的工况环境下,巡检机器人可以有效代替人工进行设备的检测与维护。为了实现巡检机器人在复杂环境中的自动避障作业,提出了基于YOLOv5深度学习网络模型的红外与可见光图像匹配识别技术,使机器人能够识别并分类各... 详细信息

在电网、桥梁等较为复杂的工况环境下,巡检机器人可以有效代替人工进行设备的检测与维护。为了实现巡检机器人在复杂环境中的自动避障作业,提出了基于YOLOv5深度学习网络模型的红外与可见光图像匹配识别技术,使机器人能够识别并分类各种障碍物,包括活体和非活体障碍。在实际巡检作业中,机器人同时配置红外相机和普通相机实时监测其前方环境。基于大量数据的训练后的YOLOv5网络模型,机器人能够快速、准确识别并判断前方障碍物并进行分类,平均识别精度高达99.2%。机器人不仅能够识别障碍物的性质,还能采取相应的主动动作来应对不同情况。实验结果充分证明了这种基于多种图像信息的综合避障方法的有效性。机器人能够在不同场景中检测、分类和规避障碍物,从而提高了其自主性和适应性。该技术在自动化巡检、安全监测以及救援任务等领域具有广泛的应用前景,为机器人技术的不断发展提供了有力支持。

关键词：自动化避障红外目标检测 YOLOv5 深度学习

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Fuzzy recognition of missile borne multi-line array infrared detection based on size calculating

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Defence technology（防务技术） 2021年第4期17卷 1135-1142页

作者： Bing-shan Lei Jing Li Wei-na Hao Ke-ding Yan School of Mechanical and Electrical Engineering Xi’an Technological UniversityXi’an710021China School of Electronic and Information Engineering Xi’an Technological UniversityXi’an710021China Key Laboratory of Education Ministry for Modern Design&Rotor-Bearing System Xi’an Jiaotong UniversityXi’an710049China

In order to improve the infrared detection and discrimination ability of the smart munition to the dynamic armor target under the complex background,the multi-line array infrared detection system is established based on the combination of the single unit infrared *** surface dimension features of ground armored targets are identified by size calculating solution *** signal response value and the value of size calculating are identified by the method of fuzzy recognition to make the fuzzy classification judgment for armored *** to the characteristics of the target signal,a custom threshold de-noising function is proposed to solve the problem of signal *** multi-line array infrared detection can complete the scanning detection in a large area in a short time with the characteristics of smart munition in the steady-state scanning *** method solves the disadvantages of wide scanning interval and low detection probability of single unit infrared *** reducing the scanning interval,the number of random rendezvous in the infrared feature area of the upper surface is increased,the accuracy of the size calculating is *** experiments results show that in the fuzzy recognition method,the size calculating is introduced as the feature operator,which can improve the recognition ability of the ground armor target with different shape size.

关键词： Multi-line array infrared detection Size calculating Custom threshold de-noising Fuzzy comprehensive discrimination algorithm

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infrared point target detection based on multi-label generative MRF model

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infrared PHYSICS & technology 2017年 83卷 188-194页

作者： Wang, Longguang Lin, Zaiping Deng, Xinpu Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

infrared point target detection based on markov random field (MRF) is mainly formulated as a binary classification problem, leading to a poor adaptability to complex background with high false alarm rate. This paper formulates the infrared point target detection as a multi-classification problem, and proposes a detection method based on multi-label generative MRF (MG-MRF) model. First, the MG-MRF model is proposed and the optimal label configuration of the infrared image is derived using iterated condition mode (ICM). Second, the pointwise adaptive filter is structured utilizing local labels to suppress the background clutter. Finally, an adaptive threshold is utilized to segment the target in the residual image. The experimental results on various backgrounds demonstrate that the detection method based on MG-MRF has a strong suppression of false alarm with superior performance in terms of accuracy and efficiency. (C) 2017 Elsevier B.V. All rights reserved.

关键词： infrared point target detection Markov random field Generative MRF model Background clutter suppression

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