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Real Time Thermal image Based Machine Learning Approach for Early Collision Avoidance System of Snowplows

Journal of Intelligent Learning Systems and Applications 2024年第2期16卷 107-142页

作者： Fletcher Wadsworth Suresh S. Muknahallipatna Khaled Ksaibati Department of Electrical Engineering and Computer Science University of Wyoming Laramie WY USA Department of Civil and Architectural Engineering University of Wyoming Laramie WY USA

In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.

关键词： Convolutional Neural Networks Residual Networks Object Detection image processing Thermal Imaging

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Data-driven polarimetric imaging: a review

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Opto-Electronic Science 2024年第2期3卷 1-44页

作者： Kui Yang Fei Liu Shiyang Liang Meng Xiang Pingli Han Jinpeng Liu Xue Dong Yi Wei Bingjian Wang Koichi Shimizu Xiaopeng Shao School of Optoelectronic Engineering Xidian UniversityXi’an 710071China School of Physics Xidian UniversityXi’an 710071China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu 808-0135Japan Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Hangzhou Institute of Technology Xidian UniversityHangzhou 311200China

This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest.Polarization information is increasingly incorporated into convolutional neural networks(CNN)as a supplemental feature of objects to improve performance in computer vision task applications.Polarimetric imaging and deep learning can extract abundant information to address various challenges.Therefore,this article briefly reviews recent developments in data-driven polarimetric imaging,including polarimetric descattering,3D imaging,reflection removal,target detection,and biomedical imaging.Furthermore,we synthetically analyze the input,datasets,and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages.We also highlight the significance of data-driven polarimetric imaging in future research and development.

关键词： deep learning polarimetric imaging image processing

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Advancements in Photovoltaic Panel Fault Detection Techniques

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Journal of Materials Science and Chemical Engineering 2024年第6期12卷 1-11页

作者： Junyao Zheng Duke University Durham North Carolina USA

This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.

关键词： Photovoltaic Panels Fault Detection Deep Learning image processing

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Automated Extraction and Analysis of CBC Test from Scanned images

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Journal of Software Engineering and Applications 2024年第2期17卷 129-141页

作者： Iman S. Alansari Computer Science Department College of Computer Science and Engineering Taibah University Madinah Saudi Arabia

Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.

关键词： image processing Optical Character Recognition Tesseract OCR Health Care Application

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Detection and processing of molten-pool image of electron-beam deep-penetration welding based on visual sensing

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China Welding 2011年第4期20卷 17-21页

作者：张秉刚石铭霄陈国庆冯吉才 State Key Laboratory of Advanced Welding Production Technology Harbin Institute of Technology Harbin 150001. State Key Laboratory of Gansu Advanced Non-ferrous Metal Materials Lanzhou University of Technology Lanzhou 730050.

A visual sensing system was developed. The system is suitable for titanium-alloy electron-beam welding, and senses and detects molten-pool dynamic processes. A suite of processing programs for colored molten-pool images in titanium-alloy electron-beam welding was developed using Matlab software; molten-pool edge images are completely obtained using the program. The Matlab software was used to write a program which could extract the molten-pool width. The functional relationship between the molten-pool width and penetration under the experimental conditions was obtained by a curve-fitting method, and provided the theoretical basis for further penetration control.

关键词： electron-beam welding visual sensing image processing curve fitting

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Acquisition and processing of coaxial image of molten pool and keyhole in Nd:YAG laser welding with high power

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China Welding 2004年第1期13卷 51-55页

作者：秦国梁齐秀滨杨永波王旭友林尚扬 State Key Laboratory of Advanced Welding Production Technology Harbin Institute of Technology Harbin Welding Institute

An experimental setup of acquiring the coaxial visual image of the molten pool and keyhole in high power Nd:YAG laser welding is introduced in this paper. It is one of the most difficult problems in acquiring coaxial image that the coaxial imaging signal of molten pool and keyhole must be separated from the laser beam with high power. This problem was resolved by designing a dichroitic spectroscope. The characteristics of imaging signal were analyzed and the coaxial image of molten pool and keyhole was acquired. A smoothing filter and a homomorphic filter were designed to remove the low frequency noise and to enhance the image according to the characteristics of imaging signal. At last, edges of molten pool and keyhole were detected and extracted based on image segmentation with threshold.

关键词： laser welding molten pool keyhole coaxial visual image image processing

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image processing and Parameters Measurement of Internal Taper Thread Based on Industrial CT Technology

Image Processing and Parameters Measurement of Internal Tape...

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第三届国际仪器科学技术学术研讨会

作者： LIU Qing-min WANG Long-shan CHEN Xiang-wei CUI Zhi College of Mechanical Science and Engineering Jilin University Changchun 130025 China

In order to perform non-damage detection and parameters measurement on internal taper thread of electrode in one system, a new non-contact measurement method based on industrial CT technique is put fo

关键词： industrial CT image processing measurement internal taper thread error

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Development and application of high-precision multifunction astronomical plate digitizers in China

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Astronomical Techniques and Instruments 2024年第1期1卷 71-75页

作者： Meiting Yang Yong Yu Liangliang Wang Zhengjun Shang Changshun Liu Lixin Zheng Zhendong Chen Jing Yang Dongmei Da Shan Dong Shiyin Shen Zhenghong Tang Jianhai Zhao Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China University of Chinese Academy of Sciences Beijing 100049China

Before charge-coupled device detectors became widely employed in observational astronomy,for more than a hundred years,the main detection method was photography on astronomical glass plates.Recently,in order to preserve these historical data and maintain their usability,the International Astronomical Union has appealed to all countries for global digitization of astronomical plates by developing or adopting advanced digitization technology.Specialized digitizers with high precision and high measuring speed represent key equipment for this task.The Shanghai Astronomical Observatory and the Nishimura Co.,Ltd in Japan cooperated between 2013 and 2016 to develop the first Chinese high-precision astronomical plate digitizer,which was then used for complete digitization of all nighttime-observation astronomical plates in China.Then,in 2019–2021,the Shanghai Astronomical Observatory independently developed new models of plate digitizers that enabled countries such as Uzbekistan and Italy to digitize their astronomical plates.Additionally,a new high-precision and multifunction digitizer was also used to digitize valuable microscope slides from the Shanghai Natural History Museum,providing a successful example of cross-domain application of high-precision digitization technology.

关键词： Astrometry-instrumentation Detectors-methods Data analysis-techniques image processing

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Introductory remote sensing : digital image processing and applications

Introductory remote sensing : digital image processing and a...

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2000年

作者： Gibson Paul.

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image processing : +the fundamentals

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1000年

作者： Maria Petrou Costas Petrou.

Includes bibliographical references and index.

ISBN: (纸本)047074586X (cloth);9780470745861 (cloth)

Includes bibliographical references and index.

关键词： image processing

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