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2nd IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Zou, Ziyan Yin, Kuiying Nanjing Research Institute of Electronic Technology Nanjing China

ISBN: (纸本)9781665428767

In the target detection task of brain-computer interface based on the fast sequence visual presentation (RSVP), P300 is usually used as the most effective feature to capture the target image in the rapidly presented image stream. For online rsvp experiments, the target detection speed is one of the important parameters in the application. For EEG data from whole brain channels, too many channels will bring too much redundant information, which will bring more time complexity and invalid features to the data processing. In the p300-based target detection experiment, people usually simply select the channel data of the central area of the p300 fixed brain area for target detection in the experiment, but there is no detailed analysis of the selection of different brain areas and the quantitative comparison of results. This paper selects the frontal and parietal areas, and does an interpretation experiment of infrared ship images. The experimental results of different brain areas are compared and analyzed offline. Compared with other electrode selection schemes, the EEG signals of 53 electrodes related to frontal region, parietal region and central region reached the highest classification accuracy, the accuracy is 89.38%. In addition, the test results may provide some ideas for the reduction of channels in wearable devices. © 2021 IEEE.

关键词： Electroencephalography

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基于多级特征增强融合的红外飞机目标检测方法

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航空学报 2023年第22期44卷 42-56页

作者：张毅张焱张宇张勇刘荻国防科技大学电子科学学院长沙410073

红外飞机目标检测识别在无人侦察、目标制导以及航班监控等领域有广阔的应用前景。为解决红外飞机目标检测识别中目标可用特征少、类间差异小导致的精确识别难度高的问题,提出了一种基于多级特征增强融合的网络模型(MFEFNet)。首先,设... 详细信息

红外飞机目标检测识别在无人侦察、目标制导以及航班监控等领域有广阔的应用前景。为解决红外飞机目标检测识别中目标可用特征少、类间差异小导致的精确识别难度高的问题,提出了一种基于多级特征增强融合的网络模型(MFEFNet)。首先,设计了一种局部与全局特征增强融合模块(LGFE),模块通过设计坐标注意力机制和全局像素注意力机制将深层语义特征和底层细节特征进行增强融合,以自顶向下方式实现深层语义动态指导底层局部特征增强,进而自适应强化网络对红外飞机目标信息的表征;然后,设计了一种全局拓展模块(GEM),在特征金字塔的基础上进一步聚合中间特征图多尺度上下文信息,提高网络对多类别目标的鉴别能力;最后,将GEM模块和LGFE模块进行级联,保持特征长距离依赖关系,产生联合效应,进一步提高网络对目标的表征能力和分类决策能力,有效解决红外飞机目标特征缺失以及类间差异小等问题。此外,本文基于迁移开发了新的地面红外飞机数据集,并通过Gram距离分析了迁移数据与真实红外数据在特征层面上的一致性。在地面红外飞机数据集上的实验结果表明:MFEFNet各个模块是有效的,与其他先进算法相比,本算法对地面红外飞机目标识别的平均均值精度(mAP)提升4.3%以上,识别精确度取得了明显优势。

关键词：红外飞机目标识别机器视觉特征增强融合全局扩展融合注意力机制

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Immune coordination deep network for hand heat trace extraction

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infrared PHYSICS & technology 2022年 127卷

作者： Zhou, Zijie Zhang, Baofeng Yu, Xiao Tianjin Univ Technol Sch Comp Sci & Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China

The recognition and detection of infrared heat trace plays an important role in the search and tracking fields of criminal investigation and military. However, due to the irregular contact area and fuzzy depth of heat trace, the existing methods cannot accurately extract the trace target. Based on the biological immune coordination mechanism, an immune coordination deep network (ICDNet) for hand heat trace extraction is proposed in this paper. It is composed of small-scale intelligent recognition method based on immune system coordination mechanism and neural immune coordination recognition method based on neural system and immune system coordination mechanism. The former divides the fuzzy area of the heat trace into multiple image patches to realize feature extraction and classification of corresponding pixels. The latter extracts the characteristics of heat trace, discriminates the type of heat trace, and obtains the prior information of heat trace. Combined with the recognition results, the infrared heat trace target is extracted. Extensive experiments on the infrared hand trace dataset demonstrate the effectiveness of our method. We achieve 91.50% mIoU on the test set, which is 23.65% higher than the latest methods.

关键词： infrared hand heat trace image infrared thermal trace extraction Immune coordination target extraction mechanism

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Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary classification with CNN

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SENSORS 2022年第24期22卷 10004-10004页

作者： Siami, Mohammad Barszcz, Tomasz Wodecki, Jacek Zimroz, Radoslaw AMC Vibro Sp Zoo Pilotow 2e PL-31462 Krakow Poland AGH Univ Sci & Technol Dept Robot & Mechatron Al Mickiewicza 30 PL-30059 Krakow Poland Wroclaw Univ Sci & Technol Fac Geoengn Min & Geol Dept Min PL-50370 Wroclaw Poland

Mechanical industrial infrastructures in mining sites must be monitored regularly. Conveyor systems are mechanical systems that are commonly used for safe and efficient transportation of bulk goods in mines. Regular inspection of conveyor systems is a challenging task for mining enterprises, as conveyor systems' lengths can reach tens of kilometers, where several thousand idlers need to be monitored. Considering the harsh environmental conditions that can affect human health, manual inspection of conveyor systems can be extremely difficult. Hence, the authors proposed an automatic robotics-based inspection for condition monitoring of belt conveyor idlers using infrared images, instead of vibrations and acoustic signals that are commonly used for condition monitoring applications. The first step in the whole process is to segment the overheated idlers from the complex background. However, classical image segmentation techniques do not always deliver accurate results in the detection of target in infrared images with complex backgrounds. For improving the quality of captured infrared images, preprocessing stages are introduced. Afterward, an anomaly detection method based on an outlier detection technique is applied to the preprocessed image for the segmentation of hotspots. Due to the presence of different thermal sources in mining sites that can be captured and wrongly identified as overheated idlers, in this research, we address the overheated idler detection process as an image binary classification task. For this reason, a Convolutional Neural Network (CNN) was used for the binary classification of the segmented thermal images. The accuracy of the proposed condition monitoring technique was compared with our previous research. The metrics for the previous methodology reach a precision of 0.4590 and an F1 score of 0.6292. The metrics for the proposed method reach a precision of 0.9740 and an F1 score of 0.9782. The proposed classification method considerably impro

关键词： belt conveyor condition monitoring overheated idlers thermal imaging image classification convolutional neural network binary classification

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基于目标特征分布增强卷积神经网络的红外目标检测算法

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导航与控制 2024年第1期23卷 97-106,62页

作者：丁胜男李威蔡立明李蒙胡常青航天时代(青岛)海洋装备科技发展有限公司青岛266237 北京航天控制仪器研究所北京100039

为了实现对水上红外弱小目标的探测,并减少由红外图像信噪比低、目标与背景的红外特征差异小等问题对检测结果的影响,通过结合红外图像的成分、纹理及目标形状特征,提出了基于目标特征分布增强卷积神经网络的红外目标检测(TFD_CNN)算法... 详细信息

为了实现对水上红外弱小目标的探测,并减少由红外图像信噪比低、目标与背景的红外特征差异小等问题对检测结果的影响,通过结合红外图像的成分、纹理及目标形状特征,提出了基于目标特征分布增强卷积神经网络的红外目标检测(TFD_CNN)算法。该算法包含目标特征分布学习与深度神经网络,具备滤除红外图像中噪声的能力,并深度挖掘红外图像中目标的边缘、纹理及形状信息,提升了卷积神经网络的分类精度。通过与4种算法进行实验对比,TFD_CNN算法分类准确率为96%,高于其他算法。结果表明:TFD_CNN算法具备对红外图像中落水人员与船只的分类能力。

关键词：红外弱小目标目标特征分布学习深度学习目标分类

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Robust Ship detection in infrared Images through Multiscale Feature Extraction and Lightweight CNN

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SENSORS 2022年第3期22卷 1226-1226页

作者： Miao, Rui Jiang, Hongxu Tian, Fangzheng Beihang Univ Sch Comp Sci & Engn Beijing Key Lab Digital Media Beijing 100191 Peoples R China

The sophistication of ship detection technology in remote sensing images is insufficient, the detection results differ substantially from the practical requirements, mainly reflected in the inadequate support for the differentiated application of multi-scene, multi-resolution and multi-type target ships. To overcome these challenges, a ship detection method based on multiscale feature extraction and lightweight CNN is proposed. Firstly, the candidate-region extraction method, based on a multiscale model, can cover the potential targets under different backgrounds accurately. Secondly, the multiple feature fusion method is employed to achieve ship classification, in which, Fourier global spectrum features are applied to discriminate between targets and simple interference, and the targets in complex interference scenarios are further distinguished by using lightweight CNN. Thirdly, the cascade classifier training algorithm and an improved non-maximum suppression method are used to minimise the classification error rate and maximise generalisation, which can achieve final-target confirmation. Experimental results validate our method, showing that it significantly outperforms the available alternatives, reducing the model size by up to 2.17 times while improving detection performance be improved by up to 5.5% in multi-interference scenarios. Furthermore, the robustness ability was verified by three indicators, among which the F-measure score and true-false-positive rate can increase by up to 5.8% and 4.7% respectively, while the mean error rate can decrease by up to 38.2%.

关键词： ship detection multiscale feature extraction lightweight CNN robustness

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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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A Vehicle target detection Method Based on Feature Level Fusion of infrared and Visible Light Image

A Vehicle Target Detection Method Based on Feature Level Fus...

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第34届中国控制与决策会议

作者： Dong Xin Lixin Xu Huimin Chen Xu Yang Ruiheng Zhang School of Mechatronical Engineering Beijing Institute of Technology

ISBN: (纸本)9781665478977

Single-mode recognition method remains a difficulty problem in target detection and recognition of road vehicle targets in complex urban ***,using the advantages of obtaining different feature information from infrared and visible images in different situations is *** propose a feature level infrared and visible image fusion target detection method based on deep *** method first obtains the registered infrared visible image,extracts the image features respectively through two main feature extraction networks,passes through the feature fusion layer,passes into the feature pyramid network to obtain the effective feature layer,and then carries out classification prediction and regression *** the test set,the mAP of the fusion method is 0.89,which is higher than that using only visible images(the mAP is 0.82) and only infrared images(the mAP is 0.79) on the same test *** the same time,in the night environment,the mAP of the fusion method is much higher than other deep learning *** experimental results show that the infrared and visible image fusion target detection method realized in this paper has certain advantages over the traditional methods and has a good application prospect.

关键词： infrared image Visible image Feature fusion Object detection

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基于深度学习的红外弱小目标检测算法研究综述

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激光与红外 2022年第9期52卷 1274-1279页

作者：王恒慧曹东赵杨杨阳中国空气动力研究与发展中心计算空气动力研究所四川绵阳621000

目标检测技术是安防监控、预警探测、遥感成像等装备的核心要素,也是当前深度学习研究领域的热点之一。红外探测系统通过被动接收物体发射的红外电磁波进行成像,具备温度灵敏度高、探测距离远、被动探测隐蔽性强等优点,在目标探测领域... 详细信息

目标检测技术是安防监控、预警探测、遥感成像等装备的核心要素,也是当前深度学习研究领域的热点之一。红外探测系统通过被动接收物体发射的红外电磁波进行成像,具备温度灵敏度高、探测距离远、被动探测隐蔽性强等优点,在目标探测领域有广泛的应用。文中从红外弱小目标图像的特点出发,针对基于深度学习的视觉图像目标检测算法进行分类描述,并对深度学习在红外弱小目标检测中的有效手段进行总结,最后对未来的发展趋势做出展望。

关键词：红外弱小目标目标检测深度学习

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基于近红外光谱技术及ELM对小麦中不同生长阶段米象的分类识别

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河南工业大学学报（自然科学版） 2023年第1期44卷 104-111页

作者：鲁玉杰王文敬张俊东王争艳卢少华河南工业大学粮油食品学院河南郑州450001 江苏科技大学粮食学院江苏镇江212100

对粮食中隐蔽性害虫的早期诊断和检测,不仅可以减少因害虫取食造成的粮食产后损失,还可以减少化学药剂的使用,对于保证粮食品质和减少环境污染具有重要的意义。基于近红外光谱技术与极限学习机(ELM)构建小麦中不同生长阶段米象的分类识... 详细信息

对粮食中隐蔽性害虫的早期诊断和检测,不仅可以减少因害虫取食造成的粮食产后损失,还可以减少化学药剂的使用,对于保证粮食品质和减少环境污染具有重要的意义。基于近红外光谱技术与极限学习机(ELM)构建小麦中不同生长阶段米象的分类识别模型,采集未感染小麦和感染米象小麦的近红外光谱数据,选择SNV+De-trending的组合对原始光谱数据进行预处理,使用主成分分析(PCA)方法对光谱数据进行降维特征提取,利用ELM和支持向量机(SVM)建立分类识别模型。结果表明:ELM模型训练时间仅需0.062 5 s,总体分类准确率为90%,0、6、24和27 d的识别率为100%,10~20 d的幼虫期识别率偏低,20 d时识别率最低,为65%;SVM模型运行时间为3.38 s,分类准确率为85.42%,ELM模型较SVM模型的运行时间和分类准确率都有所提高。因此,ELM分类识别模型能够快速准确地判断小麦有无米象,以及分类识别小麦中不同生长发育阶段的米象。

关键词：近红外隐蔽性害虫极限学习机分类米象早期诊断

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