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Deconstruction of Related Technologies of Ground image processing Based on High-Resolution Satellite Remote Sensing images

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Mobile Information Systems 2023年第1期2023卷

作者： Feng, Kai Wu, Yi Zhang, Rui Tang, Yajuan Shaanxi Academy of Aerospace Technology Application Co. Ltd Shaanxi Xi’an710100 China

The Earth observation system heavily relies on sophisticated remotely sensed satellites, an important means to obtain global high-precision geospatial products and an important strategic area for the world’s major scientific and technological powers to develop. Although China’s satellites currently have real-time or quasi-real-time observations with a high temporal resolution, there are still a lot of gaps between their positioning accuracy and the world’s advanced level. This essay aims to study an efficient ground image processing technology and apply it to high-resolution satellite remote sensing images. The convolutional neural network is an efficient deep learning method for image recognition and feature extraction. In this essay, people use a convolutional neural network (CNN) to identify ground images, use a support vector machine (SVM) to classify and summarize images, and then use a Kalman filter for noise reduction, so as to obtain sophisticated remotely sensed images. In the experiment, 100 satellite remote sensing images in the GeoimageDB database were selected for the simulation test, the images were divided into 5 types, and their recognition accuracy, classification accuracy, image signal-to-noise ratio, and resolution were analyzed. The results show that the accuracy of CNN’s recognition of different types of images is up to about 94%, and the lowest is about 85%. The accuracy of the SVM for image classification is above 80%, and the highest is about 95%. The SNR of the image after noise reduction is basically above 6.5, and some even reach above 8.0. The resolution of the image is basically above 800ppi, and the highest even reaches an ultra-high resolution of 1400ppi. Overall, the processed images are of high quality. This shows that this essay uses CNN for image recognition and then uses an SVM for classification, and finally, the method of denoising the image has certain feasibility and has achieved good results through experiments. Copyright © 2

关键词： image recognition

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deep Neural Networks Based Object Detection for Road Safety Using YOLO-V3 4th

Deep Neural Networks Based Object Detection for Road Safety ...

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4th International Conference on Smart Computing and Informatics, SCI 2020

作者： Tattari, Jalaja Donthi, Vineeth Reddy Mukirala, Dheeraj Komal Kour, S. Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9789811608773

Traffic safety plays a pivotal role in the society, and hence, there is a grave need for streamlining the protocols and measures taken in our country. This paper gives a comprehensive solution to the situation at hand, a new approach to ‘road safety’ based on seatbelt and helmet object detection using deep neural networks. The pipeline consists of using the deep learning frameworks and ‘You Only Look Once V3 (YOLO-V3)’, which is a convolutional neural network-based algorithm, for a hassle-free "real-time object detection." Detection systems in the past re-engineer classifiers or localizers to perform detection. The paper focuses on a single neural network applied to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. Popular image data sources such as the UCI Machine learning Repository and Google Open image Datasets were explored, and a dataset was created, which underwent preprocessing. The developed model is very versatile, compact, and was tested on various edge cases after which the model achieved an overall accuracy of 86.3% which was on a higher note compared to the previous works. The program can be readily deployed on CCTV, Webcams, or local computers. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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Sports fatigue detection based on deep learning 14

Sports fatigue detection based on deep learning

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14th International Congress on image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Guan, Xiaole Lin, Yanfei Wang, Qun Liu, Zhiwen Liu, Chengyi Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Beijing M&B Elect Instruments Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665400046

Moderate exercise is good for human health. However, when the exercise intensity exceeds a certain level, it will be harmful to the human body. Therefore, precise control and adjustment of exercise load can ensure athletes' sports safety and improve their competitive performance. In this work, we have developed wearable exercise fatigue detection technology to estimate the human body's exercise fatigue state using real-time monitoring of the ECG signal and Inertial sensor signal of the human body. 14 young healthy volunteers participated in the running experiment, wearing ECG acquisition equipment and inertial sensors. ECG, acceleration and angular velocity signals were collected to extract features. And then Bidirectional long and short-term memory neural network (Bi-LSTM) was used to classify three levels of sports fatigue. The results showed that the recognition accuracy of the user-independent model was 80.55%. The experimental results verified the effectiveness of the algorithm.

关键词： Sports fatigue recognition acceleration velocity angular velocity ECG HRV deep learning Bi-LSTM

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deep learning-enabled far-infrared active imaging and video-surveillance through fire 3

Deep learning-enabled far-infrared active imaging and video-...

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Multimodal Sensing and Artificial Intelligence: Technologies and Applications III 2023

作者： Bianco, Vittorio Běhal, Jaromír Mazzeo, Pier Luigi Valentino, Marika Spagnolo, Paolo Miccio, Lisa Distante, Cosimo Ferraro, Pietro Via Campi Flegrei 34 NA Pozzuoli80078 Italy University of Naples "Federico II" Department of Chemical Materials and Production Engineering P.le Tecchio 80 Naples80125 Italy Via Monteorni snc University Campus Lecce73100 Italy University of Naples "Federico II" Department of Electric and Information Technologies Engineering Via Claudio 21 Naples80125 Italy

ISBN: (纸本)9781510664517

Achieving clear vision through smoke and flames is a highly pursued goal to better manage intervention priorities and to allow first responders operating safely during fire accidents. Here we show active far-infrared systems to image static/moving targets through fire with different imaging performance and field-portability characteristics. Low-coherence infrared systems and high-coherence holographic sensors will be discussed. We show that a pre-trained convolutional neural network can detect the presence of a person hidden behind fire in real-time, accurately, even when the system is not able to reject the flame contributions in full, being suitable for video-surveillance applications. © 2023 SPIE.

关键词： Infrared radiation

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Secure MRI Brain image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach

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JOURNAL OF SENSORS 2022年第0期2022卷

作者： deepthi, S. Aruna Rao, E. Sreenivasa Giriprasad, M. N. JNTUA Anantapuramu Anantapuramu India Vasavi Coll Engn Hyderabad India

In recent times, the medical image processing solves several clinical issues by inspecting the visual images, which are generated in the clinical health care units. The main objective of the research is to gain valuable information from the images for better clinical diagnosis. In the biomedical engineering domain, the medical image analysis is an emerging research topic. In the recent decades, the use of medical images is highly growing, which are acquired from different image modalities;so, there is a necessity for data compression for transmission, storage, and management of digital medical image datasets. Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine learning tools for automatically learning the feature vectors from the huge medical datasets. The automated deep learning models are effective compared to the conventional handcrafted features. In addition to this, a wireless sensor network (WSN) is used to create a primary health care scheme, which brings patient data together and expands the medical conveniences, whereas the WSN design must comprise of sensor nodes, because it consumes less power and resources at a relatively low cost;so, it is essential for implementing the Raspberry Pi-based WSN nodes. The sensor nodes are important for limited battery capacity and to transmit the vast amount of medical data. The proposed work is broadly classified into two categories such as (i) the medical image compression algorithm is developed using the deep learning model based on autoencoders and restricted Boltzmann machines (RBM) and (ii) implementation of the WSN sensors nodes with Raspberry Pi and Messaging Queue Telemetry Transport (MQTT) Internet of Things (IoT) protocol for secure transmission of the medical images. The experimental results are evaluated using the standard performance metrics like peak signal to noise ratio (PSNR) and presented a real-time Linux (RTL) implementat

关键词： Signal to noise ratio

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Countries flags detection based on local context network and color features

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MULtimeDIA TOOLS AND APPLICATIONS 2021年第10期80卷 14753-14765页

作者： Said, Yahia Barr, Mohammad Northern Border Univ Coll Engn Elect Engn Dept Ar Ar Saudi Arabia Univ Monastir Fac Sci Monastir Lab Elect & Microelect LR99ES30 Monastir Tunisia

Countries flags are characterized by a combination of special colors. Building an automatic country flag detector is a hard task because of many challenges like deformation and difference in point of view. Motivated by the unique feature of the country flag colors and the power of deep learning models, we propose to use color-based features and a Convolutional Neural Network (CNN) with a special local context neural network to perform the countries flags detection task. The proposed approach aims to enhance the performance of the ordinary Convolutional Neural Network by adding a local context neural network to enhance the localization task and adding a color-based descriptor to enhance the identification task. The color-based descriptor was used to focus on the color features because of its importance for the studied task. The Convolutional Neural Network was proposed to extract more relevant features for both localization and identification tasks. The local context network was used to localize the flag in the image. In order to train and evaluate the proposed approach, we propose to build a custom dataset for the world countries' flags. The proposed dataset counts 100 images for each country flag with a total of 20,000 images. The evaluation of the proposed approach proves its efficiency by achieving a mean Average Precision of 89.5% and a real-time processing speed. The achieved results have proved the efficiency of the proposed method. The proposed enhancement was very effective that allows the achievement of high accuracy.

关键词： Flag detection deep learning Convolutional Neural Network Local context network Color features

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An Adaptive Vision-based Outdoor Car Parking Lot Monitoring System 8

An Adaptive Vision-based Outdoor Car Parking Lot Monitoring ...

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8th IEEE International Conference on Communications and Electronics (IEEE ICCE)

作者： Thang Nguyen Thom Tran Tho Mai Hanh Le Cuong Le Doan Pham Kieu-Ha Phung Hanoi Univ Sci & Technol Hanoi Vietnam

ISBN: (数字)9781728154718

ISBN: (纸本)9781728154718

Monitoring system installed at a parking field is to update real time data of vacant parking lots to central management and to provide search and book services to car drivers. Camera attached with image processing unit running deep learning based algorithms to detect vacant/occupied parking lots is promising with rather high accuracy. In this work, we propose a novel solution using mAlexNet, a CNN-based model, to classify vacant or occupied state of each parking lot snapshot, with a pre-processing stage, camera adjustment method, enabling the solution to be automatically adaptive with the condition setting variations. The solution is capable to run on resource constrained processors like Rasberry Pi 4 and has been tested on parking fields at our university campus, showing the accuracy of over 97% and rather fast processing pace of 0.743 seconds in average for each frame capturing 24 parking lots.

关键词： Convolution Neural Network (CNN) parking management image warping classification

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Unified deep learning approach for prediction of Parkinson's disease

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IET image processing 2020年第10期14卷 1980-1989页

作者： Wingate, James Kollia, Ilianna Bidaut, Luc Kollias, Stefanos Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Natl Tech Univ Athens Sch Elect & Comp Engn 9 Iroon Polytech St Athens 15780 Greece

The study presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by deep convolutional and recurrent neural networks when trained with medical images, such as magnetic resonance images and dopamine transporters scans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson's across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson's, using different medical image sets from real environments.

关键词： recurrent neural nets diseases medical image processing biomedical MRI learning (artificial intelligence) unified deep learning approach medical imaging recurrent neural networks magnetic resonance images trained DNN transfer learning medical environments Parkinson disease diagnosis dopamine transporter scans

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Single image Super Resolution Reconstruction Algorithm Based on deep learning

Single Image Super Resolution Reconstruction Algorithm Based...

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2020 International Conference on Artificial Intelligence, Computer Networks and Communications, AICNC 2020

作者： Kuang, Qing Institute of Technology East China Jiaotong University 330052 China

Super resolution image reconstruction technology is the core technology in image processing, which provides technical support for most of the intelligent devices for target tracking and target detection. However, the existing image acquisition stage will cause extra high cost to improve the image resolution. Optimization algorithm is one of the best ways to solve this kind of problem. At present, there is no substantial breakthrough in this field in China. Therefore, this paper studiessingle image super-resolution reconstruction algorithm based on deep learning. In this paper, the application of super-resolution reconstruction algorithm in single image is discussed. The analysis shows that the application of deep learning in this field is shallow and has a large optimization space. Especially for the existing main problems, the traditional algorithm is optimized and improved. According to the demand of resolution reconstruction, combined with the latest deep learning method, the accuracy and robustness of the algorithm are further improved. At the same time, it effectively improves the comprehensive performance of the model. In order to further verify the actual performance of the proposed algorithm, the corresponding comparative experiments are established. Theexperimental results show that the proposed algorithm has obvious advantages over the traditional fsrcnn algorithm, especially when the PSNR of dataset 4 is 2, 3, and 4 times, it can increase 0.41 dB, 0.58 dB and 0.52 DB respectively. Analysis shows that this algorithm has obvious advantages and achieves ideal results. © Published under licence by IOP Publishing Ltd.

关键词： Target tracking

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A framework for synthetic image generation and augmentation for improving automatic sewer pipe defect detection

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AUTOMATION IN CONSTRUCTION 2022年第0期137卷 1页

作者： Siu, ChunFai Wang, Mingzhu Cheng, Jack C. P. Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China Loughborough Univ Sch Architecture Bldg & Civil Engn Loughborough Leics England

Sewer pipes are essential infrastructure for discharging wastewater. Regular pipe inspection is necessary to prevent malfunction of sewer systems, for which closed-circuit television (CCTV) crawlers are commonly used to capture images of the pipe interior. As manual assessment of pipe condition is labor-intensive and timeconsuming, automated defect detection using computer vision and deep learning has been increasingly studied in recent years. However, deep learning approaches require large amount of annotated data for model training. Data collection in underground sewer pipes is expensive and difficult since they are inaccessible without the use of an inspection robot. Meanwhile, ground-truth annotation needs to be accurate and consistent, requiring massive time and expertise. This paper proposes a framework for synthetic data generation and augmentation to address the data shortage problem for sewer pipe defect detection. First, synthetic images of sewer pipes are generated by 3D modeling and simulation in virtual environment. The quality of the generated images is then enhanced using style transfer with reference to real inspection images. In addition, a contrastive learning module is developed to further improve the deep learning process for defect detection. Experiment results show that the average precision (AP) of the defect detection model is improved by 2.7% and 4.8% respectively after adding style-transferred synthetic images and applying the contrastive module. When both methods are applied, the AP of the model is boosted by 7.7%, from 22.22% to 23.92%, indicating the effectiveness of our proposed approaches. This study is expected to alleviate the burden on data collection and annotation for applying deep learning models in defect detection.

关键词： Synthetic image Data augmentation Defect detection Virtual simulation Contrastive learning deep learning Computer vision image processing Sewer pipe

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