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22nd IEEE International Conference on Data Mining (ICDM)

作者： Yao, Eric Kendziora, Christopher A. Furstenberg, Robert Breshike, Christopher J. Finton, Drew M. Kendziora, Drew C. Winston Churchill High Sch SEAP Program Potomac MD 20854 USA US Naval Res Lab Code 6365 Washington DC USA US Naval Res Lab Washington DC USA US Naval Res Lab NREIP Program Washington DC USA

ISBN: (纸本)9798350346091

We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared reflectance signatures. The infrared Backscatter Imaging Spectroscopy (IBIS) technology is used to capture these reflectance signatures through a mobile cart-based system. An infrared laser is directed toward the target surface from a standoff distance, and an MCT focal plane array collects the backscatter images from different wavelengths of laser into what is known as a hyperspectral image cube (hypercube for short). The hypercubes from our measurements are represented in a matrix of numbers. These hypercubes have a 128x128 front face representing the spatial dimensions (the physical image), and a depth of 153 representing the spectral dimension, which contains the IBIS measurements from various wavelengths of laser. These data are fed into a deep neural network machine learning algorithm for classification into specific chemicals (analytes), We propose a data accentuation technique to improve classification performance on IBIS data. Furthermore, we demonstrate the capability of machine learning algorithms to perform pixel-by-pixel discrimination of chemicals within a hypercube image, as well as the simultaneous detection of multiple different chemicals. We also demonstrate the capacity of machine learning algorithms to perform with high precision and recall under noisy conditions. The algorithm goes beyond simply identifying the presence of a chemical, in that it is also able to pinpoint the spatial location of each chemical within the field of view.

关键词： standoff detection infrared backscatter diffuse reflectance hypercube deep neural network analyte

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A Multi-Gas detection System Based on Convolutional Neural Network 43

A Multi-Gas Detection System Based on Convolutional Neural N...

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43rd Chinese Control Conference, CCC 2024

作者： Chen, Si-Wen Li, Hong-Yue Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared (NDIR) multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), and propane (C3 H8). We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network (1D-CNN) and depthwise separable convolutional neural network (DS-CNN). The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Air quality

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Research on human target detection and recognition technology of infrared thermal imaging in coal mine

Research on human target detection and recognition technolog...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Yonghui Guo CCTEG Taiyuan Research Institute Co. Ltd China Shanxi Tiandi Coal Mining Machinery Co. Ltd China China National Engineering Laboratory for Coal Mining Machinery China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

In coal mines, infrared thermal imaging technology can be used to monitor and identify underground personnel to help ensure the safety of miners. Through infrared thermal imaging technology, the body temperature distribution of miners can be monitored in real time, and abnormal conditions can be found in time, such as abnormally high or abnormally low body temperature, so that timely measures can be taken to ensure the safety of miners. In this paper, the methods of median filter, sobel operator image segmentation, HOG feature extraction and SVM classification and recognition are used to process the human walking image in coal mine underground roadway, and the test results can meet the needs of human detection and recognition in coal mine.

关键词： Temperature measurement Support vector machines Temperature sensors Image segmentation Temperature distribution Image recognition target recognition

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Hierarchical Transformer for Multi-Label Trailer Genre classification 48

Hierarchical Transformer for Multi-Label Trailer Genre Class...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cai, Zihui Ding, Hongwei Wu, Xuemeng Xu, Mohan Cui, Xiaohui Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering China

ISBN: (纸本)9781728163277

Determining the genres of a trailer is a challenging multi-label classification task. Previous studies tend to classify by CNN or RNN. Recently, Transformer based on attention mechanism has achieved better results in many research fields than CNN and RNN. Inspired by these, we propose a Hierarchical Transformer (HT). HT can process both the frame sequence (HT-F) and audio (HT-A) of trailers. Besides, a feature compression module is inserted into HT-F, and audio spectrogram segment is processed by HT-A as a whole, which can effectively reduce the data processed by the second Transformer. In order to reduce the training cost and improve the performance, we load the pre-trained weights from other related fields into some parameters of HT, and utilize the limited resources to train the remaining parameters. Experiments show that our best model outperforms state-of-the-art methods on several comprehensive metrics. © 2023 IEEE.

关键词： Hierarchical Transformer multi-label self-attention trailer genre classification transfer learning

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Lightweight infrared Object detection Network based on Improved SSD 23

Lightweight Infrared Object Detection Network based on Impro...

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Proceedings of the 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Dan Shan Songyuan Yang Zilun Zhao Xiaofeng Gao Baozhu Zhang Baomin Zhang Beijing Institute of Technology China Beijing Aerospace Institute for Metrology and Measurement Technology China Beijing Institute of Radio Measurement China Daqing Oilfield Powerlift Pump Industry Co.Ltd China Institute of Software Chinese Academy of Sciences China

ISBN: (纸本)9798400709203

Object detection involves the identification and localization of objects and serves as the foundation for accurately perceiving various targets. infrared imaging, known for its ability to perceive differences in the heat distribution of environments, has garnered attention in many fields. Similar to general object detection tasks, the goal of infrared image object detection is to identify objects of interest within the image. However, infrared images often present challenges due to the small size of the targets, limited features, and the ease that target information being confused with background information. To address this issue, this paper proposes an improved algorithm based on the SSD (Single Shot MultiBox Detector) algorithm. The algorithm modifies the size and quantity of anchor boxes at different levels within the SSD framework, making the model more inclined to select smaller targets. Additionally, we implement a series of lightweight modifications to the SSD model, reducing computational resources required for processing lower-resolution infrared images. Experimental results on two datasets demonstrate that this algorithm’s predictions are precise, with an average prediction accuracy reaching 80% and classification confidence level at 79%, surpassing the performance of existing object detection algorithms.

关键词： infrared Image

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Weed detection among soybean plants in artificial lighting environment using multispectral images and Computer Vision

Weed detection among soybean plants in artificial lighting e...

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SPIE-CLP Conference on Advanced Photonics 2023

作者： Oda, Yuri Sarreta de Oliveira, Lucas Orlandi de Paula, Samuel de Oliveira, André Orlandi Neto, Jarbas Caiado de Castro São Carlos Institute of Physics University of São Paulo Avenida Trabalhador São-carlense 400 São Carlos Brazil Department of Plant Pathology and Nematology Luiz de Queiroz College of Agriculture University of São Paulo Avenida Pádua Dias 11 Piracicaba Brazil Agrio Technology Avenida Professor Luiz Augusto De Oliveira 91 São Carlos Brazil

ISBN: (数字)9781510667396

ISBN: (纸本)9781510667389

Precision Agriculture stands out as one of the most promising areas for the development of new technologies around the world. Some advances from this area include the mapping of productivity areas and the development of sensors for climate and soil analysis, improving the smart use of resources during crop management and helping farmers during the decision-making stages. Among the problems of modern agriculture, the intensive and non-localized use of herbicides causes environmental issues, contributes to elevated costs in farmers’ budgets and results in applications of chemical substances in non-target organisms. Although there are many selective herbicide spraying systems available for use, the majority working principle is based upon chlorophyll detectors, thus not being able to distinguish crop plants from weeds with high accuracy in crop’s post-emergence herbicide applications ("green-on-green" application). The main objective of this study is to develop a multispectral camera system for in-crop weed recognition using Computer Vision techniques. The system was built with four monochromatic CMOS sensor cameras with monochromatic wavelength bandpass filters (green, red, near infrared and infrared) and a RGB camera. Soybean and weed plants images were captured in a controlled environment using an automated v-slot rail system to simulate the movement of a spray tractor in the field. infrared images presented higher precision (90.5%) and recall (89.3%) values compared to the other monochromatic bands, followed by RGB (87.0% and 86.1%, respectively) and near infrared images (83.6% and 87.9%), suggesting that infrared wavelengths plays an important role in plant detection and classification. Our results state that the combination of Computer Vision and multispectral images of plants is a more efficient approach for targeting weeds among crop plants for post-emergence herbicide applications. © 2023 SPIE.

关键词： Crops

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基于改进YOLOv5的红外车辆检测方法

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红外与激光工程 2023年第8期52卷 235-244页

作者：张学志赵红东刘伟娜赵一鸣关松河北工业大学电子信息工程学院天津300401 电磁空间安全全国重点实验室天津300308

红外图像可在低照度、恶劣天气等条件下工作,红外车辆检测技术旨在使用红外传感器来监测道路上的车辆,实现对车辆数量、车速等信息的收集与分析,该技术不仅可应用于路面车辆,还可应用于铁路、机场、港口等场景,为交通运输行业的安全和... 详细信息

红外图像可在低照度、恶劣天气等条件下工作,红外车辆检测技术旨在使用红外传感器来监测道路上的车辆,实现对车辆数量、车速等信息的收集与分析,该技术不仅可应用于路面车辆,还可应用于铁路、机场、港口等场景,为交通运输行业的安全和便捷提供了有效的技术支持。然而,由于红外图像成像原理的局限和外部环境的干扰,通常导致红外图像成像质量不理想,红外车辆检测仍然存在许多问题。文中提出了一种改进的YOLOv5模型,在YOLOv5的主干部分引入了混合注意力机制,使模型能够更好地关注研究者感兴趣的区域,抑制图像噪声的干扰。此外,在BiFPN基础上提出了一种改进的Z-BiFPN特征融合结构,融合更多的浅层信息,提高浅层信息利用率,并增加一个四分之一下采样的小目标检测层,同时将YOLOv5的检测头替换为解耦头来提升模型的检测能力。在自建的七类红外车辆数据集infrared-417上进行了实验,验证了算法的有效可行性。与原始YOLOv5相比,m AP从81.1%提升到了85.3%。

关键词：红外车辆目标检测注意力机制特征融合 YOLOv5

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infrared Unmanned Aerial Vehicle detection based on Generative Adversarial Network data augmentation

Infrared Unmanned Aerial Vehicle detection based on Generati...

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Conference on AOPC - infrared Device and infrared technology

作者： Gao, Yuan Luo, Zijuan Yu, Xuelian Ren, Kan Ye, Yunfei Chen, Qian Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing 210094 Peoples R China China Elect Technol Corp Res Inst 28 Nanjing 210007 Peoples R China

ISBN: (数字)9781510649989

ISBN: (纸本)9781510649989;9781510649972

As the application of UAVs in military and civilian fields becomes more and more widespread, the detection of UAVs in the low-altitude range has also become an important research direction. Compared with radar and visible light detection, infrared technology has become the major UAV detection method with its advantages of all-weather and long range. Most of the current infrared target detection methods are based on convolutional neural networks (CNN), which achieve target detection through feature extraction and feature classification. The performance of all such detection algorithms is highly dependent on their training set. A data set with a large number of samples and wide coverage tends to train a more robust and accurate detector. So, to obtain better detection effects, we perform data augmentation on the infrared UAV dataset by adversarial generative network (GAN). First, we extract the targets from the training set and train a GAN network, using its generator to obtain many new targets which are different from the training set samples, then we randomly extend these targets to the original dataset, and finally we retrain the detectors using the new dataset to achieve better detection. We created an infrared UAV image dataset for our experiments, with only a single target on each image. After data augmentation, multiple UAV targets are randomly generated. The experiments demonstrate that the new dataset trains the model with better detection results. And the GAN data augmentation can be combined with many advanced detectors to make a large improvement in detection.

关键词： infrared UAV detection data augmentation convolutional neural network deep learning

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Chemical vapor detection using a passive infrared bioinspired sensor 23

Chemical vapor detection using a passive infrared bioinspire...

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Conference on Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIII

作者： Major, Kevin J. Nicol, Robert R. Sanghera, Jasbinder S. Shaw, L. Brandon Samuels, Alan Davidson, Charles Ben-David, Avishai Ewing, Kenneth J. US Naval Res Lab Opt Sci Div Washington DC 20375 USA Jacobs Engn Grp Hanover MD 21076 USA US Army Combat Capabil Dev Command Chem Biol Ctr Gunpowder MD 21010 USA

ISBN: (纸本)9781510651098;9781510651081

Standoff detection of chemicals remains a crucial need for a variety of applications of importance for defense, homeland security, environmental, and industrial applications. The goal of standoff chemical sensing is to enable the identification and classification of an unknown hazardous or toxic chemical, without any operator or instrument having to come in direct contact with the chemical itself. Currently, standoff detection of chemical vapors is carried out using optical sensing techniques. Passive infrared (IR) sensors have identified chemical vapor clouds at ranges exceeding one kilometer by detecting, spectrally resolving, and analyzing scene radiance. Currently available passive IR sensors have substantial size, weight, power, and cost (SWaP-C) limitations, which reduce the number of sensors capable of being deployed in a given area, or precludes their use altogether in certain circumstances. To address these limitations, we are developing a unique passive low SWaP-C IR sensor capable of detecting chemical vapors when viewed against a cold-sky or terrestrial background. This sensor, inspired by human color vision, will use only the response through three broadband infrared optical filters to discriminate between target chemicals and background interferents. The key technology of the PBS is a commercially available pyroelectric quadrant chip sensor which contains four channels with unique bandpass IR filters installed. We demonstrate results collected using a variable temperature blackbody in the laboratory, which represents passive IR sensing against various background conditions. These results demonstrate the first step in the development of a passive bioinspired IR sensor which will use only low-cost commercially available components, and be capable of rapidly providing actionable detection of chemical vapor clouds.

关键词： infrared sensing optical sensing chemical detection standoff sensing human color vision

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短波红外偏振成像技术的研究进展

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应用光学 2023年第3期44卷 643-654页

作者：李子园金伟其北京理工大学光电学院光电成像技术与系统教育部重点实验室北京100081

与传统成像系统相比,偏振成像系统通过探测目标物在不同状态下的明显偏振差异,提高对目标物的探测和识别能力,因而被广泛应用于复杂环境或有伪装物的目标探测。特别是短波红外谱范围的信息探测可以提供人眼所不能看见的分辨率和细节,是... 详细信息

与传统成像系统相比,偏振成像系统通过探测目标物在不同状态下的明显偏振差异,提高对目标物的探测和识别能力,因而被广泛应用于复杂环境或有伪装物的目标探测。特别是短波红外谱范围的信息探测可以提供人眼所不能看见的分辨率和细节,是目前军事和民用领域的重点研究方向之一。这篇综述归纳总结了传统偏振成像系统的结构分类及其特点,探讨了新型微纳偏振成像系统的研究进展,并针对目前短波红外偏振成像技术存在的主要问题提供了可行性分析和建议。

关键词：光电成像系统偏振成像技术短波红外新型微纳结构目标探测与识别

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