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基于raspberry pi的实时流媒体直播系统设计

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曲靖师范学院学报 2024年第3期43卷 78-84页

作者：王灵芝陈颖频王朝慧陈士豪厦门城市职业学院人工智能学院福建厦门361008 闽南师范大学物理与信息工程学院福建漳州363000

在“全民直播、万物皆可播”时代背景下,阐述了一种实时流媒体直播系统的设计过程.系统以raspberry pi 4B作为主控,搭载Linux操作系统.系统由视频采集端、流媒体服务器端、客户端三部分组成.视频采集端实时获取摄像头视频数据,利用FFmpe... 详细信息

在“全民直播、万物皆可播”时代背景下,阐述了一种实时流媒体直播系统的设计过程.系统以raspberry pi 4B作为主控,搭载Linux操作系统.系统由视频采集端、流媒体服务器端、客户端三部分组成.视频采集端实时获取摄像头视频数据,利用FFmpeg协议对视频流封包并推送到流媒体服务器.服务端运行SRS流媒体服务器,提供视频存储、推送与软件更新服务.客户端支持视频流拉取、直播、片段回放功能.系统采用QT配合触摸屏设计人机交互界面,可远程进行软件更新.本系统具有成本低、部署便捷、延迟短等优点,经测试系统的整体延时小于200 ms.

关键词：流媒体直播系统 raspberry pi FFmpeg SRS流媒体服务器 QT

基于raspberry pi及Drone Kit的无人机飞行控制教学实践应用

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微型电脑应用 2024年第5期40卷 243-246页

作者：曹芸芸上海行健职业学院信息技术与智能制造学院上海200072

基于开源硬件raspberry pi,pixhawk及开源软件Drone Kit,设计了高校无人机飞行控制教学实践课程。课程中使用raspberry pi作为无人机的机载计算机与pixhawk飞控芯片协同工作,通过使用MAVLink协议通信。学生在实践操作中需要掌握硬件各... 详细信息

基于开源硬件raspberry pi,pixhawk及开源软件Drone Kit,设计了高校无人机飞行控制教学实践课程。课程中使用raspberry pi作为无人机的机载计算机与pixhawk飞控芯片协同工作,通过使用MAVLink协议通信。学生在实践操作中需要掌握硬件各接口的功能属性进行无人机组装,在调测环境下,基于Drone Kit开发无人机飞行控制程序。所提的实践课程可以提高学生的动手实践能力,新环境下的创新应用能力能加深对飞行控制技术的理论理解。

关键词：实践课程 raspberry pi Drone Kit 无人机飞行控制

Embedded System Based raspberry pi 4 for Text Detection and Recognition

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Intelligent Automation & Soft Computing 2023年第6期36卷 3343-3354页

作者： Turki M.Alanazi Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia

Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured,such as viewing angles,blurring,sensor noise,etc.However,in this paper,a prototype for text detection and recognition from natural scene images is proposed.This prototype is based on the raspberry pi 4 and the Universal Serial Bus(USB)camera and embedded our text detection and recognition model,which was developed using the Python language.Our model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detec-tor(EAST)model for text localization and detection and the Tesseract-OCR,which is used as an Optical Character Recognition(OCR)engine for text recog-nition.Our prototype is controlled by the Virtual Network Computing(VNC)tool through a computer via a wireless connection.The experiment results show that the recognition rate for the captured image through the camera by our prototype can reach 99.75%with low computational complexity.Furthermore,our proto-type is more performant than the Tesseract software in terms of the recognition rate.Besides,it provides the same performance in terms of the recognition rate with a huge decrease in the execution time by an average of 89%compared to the EasyOCR software on the raspberry pi 4 board.

关键词： Text detection text recognition OCR engine natural scene images raspberry pi USB camera

A Deep Trash Classification Model on raspberry pi 4

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Intelligent Automation & Soft Computing 2023年第2期35卷 2479-2491页

作者： Thien Khai Tran Kha Tu Huynh Dac-Nhuong Le Muhammad Arif Hoa Minh Dinh Ho Chi Minh City University of Foreign Languages and Information Technology Ho Chi Minh City700000Vietnam International University Ho Chi Minh CityVietnam-Vietnam National UniversityHo Chi Minh City70000Vietnam Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam Department of Computer Science and Information Technology University of LahoreLahorePakistan

Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most countries.Trash classi-ﬁcation from a collection of trash images can limit the overloading of garbage dis-posal systems and efﬁciently promote recycling activities;thus,development of such a classiﬁcation system is topical and urgent.This paper proposed an effective trash classiﬁcation system that relies on a classiﬁcation module embedded in a hard-ware setup to classify trash in real time.An image dataset isﬁrst augmented to enhance the images before classifying them as either inorganic or organic trash.The deep learning–based ResNet-50 model,an improved version of the ResNet model,is used to classify trash from the dataset of trash images.The experimental results,which are tested both on the dataset and in real time,show that ResNet-50 had an average accuracy of 96%,higher than that of related models.Moreover,integrating the classiﬁcation module into a raspberry pi computer,which con-trolled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste,created a complete trash classiﬁcation system.This proves the efﬁciency and high applicability of the proposed system.

关键词： Trash classiﬁcation ResNet raspberry pi internet of things(IoT) deep learning

Securing Transmitted Color Images Using Zero Watermarking and Advanced Encryption Standard on raspberry pi

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Computer Systems Science & Engineering 2023年第11期47卷 1967-1986页

作者： Doaa Sami Khafaga Sarah M.Alhammad Amal Magdi Osama ElKomy Nabil ALashin Khalid M.Hosny Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology Zagazig UniversityZagazig44519Egypt

Image authentication techniques have recently received a lot of attention for protecting images against unauthorized access.Due to the wide use of the Internet nowadays,the need to ensure data integrity and authentication increases.Many techniques,such as watermarking and encryption,are used for securing images transmitted via the Internet.The majority of watermarking systems are PC-based,but they are not very portable.Hardwarebased watermarking methods need to be developed to accommodate real-time applications and provide portability.This paper presents hybrid data security techniques using a zero watermarking method to provide copyright protection for the transmitted color images using multi-channel orthogonal Legendre Fourier moments of fractional orders(MFrLFMs)and the advanced encryption standard(AES)algorithm on a low-cost raspberry pi.In order to increase embedding robustness,the watermark picture is scrambled using the Arnold method.Zero watermarking is implemented on the raspberry pi to produce a real-time ownership verification key.Before sending the ownership verification key and the original image to the monitoring station,we can encrypt the transmitted data with AES for additional security and hide any viewable information.The receiver next verifies the received image’s integrity to confirm its authenticity and that it has not been tampered with.We assessed the suggested algorithm’s resistance to many attacks.The suggested algorithm provides a reasonable degree of robustness while still being perceptible.The proposed method provides improved bit error rate(BER)and normalized correlation(NC)values compared to previous zero watermarking approaches.AES performance analysis is performed to demonstrate its effectiveness.Using a 256×256 image size,it takes only 2 s to apply the zero-watermark algorithm on the raspberry pi.

关键词： Zero watermarking raspberry pi advanced encryption standard

New Lite YOLOv4-Tiny Algorithm and Application on Crack Intelligent Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 528-536页

作者：宋立博费燕琼 Student Innovation Center Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Conforming to the rapidly increasing market demand of crack detection for tall buildings,the idea of integrating deep network technology into wall-climbing robot for crack detection is put forward in this paper.Taking the dependence and hardware requirements when deployed on such edge devices as raspberry pi into consideration,the Darknet neural network is selected as the basic framework for detection.In order to improve the inference efficiency on edge devices and avoid the possible premature over-fitting of deep networks,the lite YOLOv4-tiny algorithm is then improved from the original YOLOv4-tiny algorithm and its structure is illustrated using Netron accordingly.The images downloaded from Internet and taken from the buildings in campus are processed to form crack detection data sets,which are trained on personal computer with the AlexeyAB version of Darknet to generate weight files.Meanwhile,the AlexeyAB version of Darknet accelerated by NNpack package is deployed on raspberry pi 4B,and the crack detection experiments are carried out.Some characteristics,e.g.,fast speed and lower false detection rate of the lite YOLOv4-tiny algorithm,are confirmed by comparison with those of original YOLOv4-tiny algorithm.The innovations of this paper focus on the simple network structure,fewer network layers,and earlier forward transmission of features to prevent over-fitting,showing the new lite neural network exceeds the original YOLOv4-tiny network significantly.

关键词： intelligent detection deep network edge device raspberry pi

Juxtapose of System Performance for Food Calorie Estimation Using IoT and Web-based Indagation

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Journal of Harbin Institute of Technology(New Series) 2024年第1期31卷 54-63页

作者： Anusuya S Sharmila K Department of Computer Science VISTASChennai 600117India Department of Computer Science Dr.MGR Janaki College for WomenChennai 600028India

A rudimentary aspect of human life is the health of an individual,and most commonly the wellbeing is impacted in a colossal manner through the consumption of food. The intake of calories therefore is a crucial aspect that must be meticulously monitored. Various health gremlins can be largely circumvented when there is a substantial balance in the number of calories ingested versus the quantity of calories expended.The food calorie estimation is a popular domain of research in recent times and is meticulously analyzed through various image processing and machine learning techniques. However,the need to scrutinize and evaluate the calorie estimation through various platforms and algorithmic approaches aids in providing a deeper insight on the bottlenecks involved,and in improvising the bariatric health of an individual. This paper pivots on comprehending a juxtaposed approach of food calorie estimation through the use of employing Convolution Neural Network(CNN)incorporated in Internet of Things(IoT),and using the Django framework in Python,along with query rule-based training to analyze the subsequent actions to be followed post the consumption of food calories in the constructed webpage. The comparative analysis of the food calorie estimate implemented in both platforms is analyzed for the swiftness of identification,error rate and classification accuracy to appropriately determine the optimal method of use. The simulation results for IoT are carried out using the raspberry pi4B model,while the Anaconda prompt is used to run the server holding the web page.

关键词： food calorie estimation convolution neural network Django framework internet of things raspberry pi

Design of Li River Water Quality Dynamic Monitoring System Based on raspberry pi and TinyML

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Journal of Computer and Communications 2023年第8期11卷 121-133页

作者： Xinyi Tang School of Computer Science and Engineering/School of Software Guangxi Normal University Guilin China

In order to solve the problem of scientific monitoring of water quality, a trophic monitoring system for Li River water quality is developed to improve the decision-making of related environmental management departments. The system is based on embedded computing, deep learning and Internet of Things technology, combined with software and hardware design, to automatically obtain real-time water quality parameters with raspberry pi equipped with sensors and positioning modules. A camera is employed to capture the screen, and yolo-tiny image recognition is implemented in the raspberry pi. Lastly, the cloud storage is used for interaction to realize real-time monitoring of water quality, real-time positioning of the boat, real-time return of image recognition and visualization. The system is proven efficient and intelligent in facilitating water quality protection.

关键词： raspberry pi Sensors YOLO-Tiny Model Cloud Development

基于raspberry pi的磷源供料监控系统的研究与实现

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基于Raspberry Pi的磷源供料监控系统的研究与实现

作者：宋正宇贵州大学

学位级别：硕士

光伏发电是未来的新能源发展趋势,光伏电池片的制备需要使用三氯氧磷。在工业生产过程中,三氯氧磷参与反应时,对环境温度有相应要求,还会生成有毒性和腐蚀性气体。国内部分厂商没有一套完善的实时环境监控系统来监控生产环境信息,预防... 详细信息

光伏发电是未来的新能源发展趋势,光伏电池片的制备需要使用三氯氧磷。在工业生产过程中,三氯氧磷参与反应时,对环境温度有相应要求,还会生成有毒性和腐蚀性气体。国内部分厂商没有一套完善的实时环境监控系统来监控生产环境信息,预防生产事故。因此,本文旨在设计一套基于raspberry pi的磷源供料监控系统。系统能够实时收集生产过程中的温度、气体信息,对液罐液位实时监控,以上信息超出正常值报警并启动应急处理设备。系统通过raspberry pi连接传感器对环境数据进行监测,摄像头对恒温箱内液罐液位监测,并使用深度学习算法对当前恒温箱内部液位状况进行识别,以上内容在显示屏上显示。通过与raspberry pi连接的蜂鸣器、风扇、制动开关等控制器实现了通过报警模块对当前情况进行提醒和即时处理。系统能够实时监控环境信息,raspberry pi自动处理信息,自动向工人发送提醒。工人还可以通过与raspberry pi连接的显示屏手动操作控制器,处理危险状况;通过对采集到的数据进行存储,还能够为企业提供更好的生产计划和应急设备规划。系统解决了部分使用三氯氧磷进行生产的厂商,在生产过程中需要手动收集数据和目视测量液位,导致的数据缺失和危险情况响应不及时的问题。系统基本达到了提高生产效率,保障了工人的人身安全和原材料的正常生产的目的。

关键词： raspberry pi 传感器多线程通信识别