基于Raspberry Pi的安全帽识别系统设计与实现

为了便于施工危险区域人员的自动化识别,提出了一种基于Raspberry Pi的安全帽识别算法。该算法将摄像头采集到的原始视频图像进行滤波、形态学等处理,再对图像中的安全帽进行识别。对于彩色安全帽,将原始图像转换到HSV空间,根据不同颜色色调阈值的设定同时识别红、黄、蓝三种颜色的安全帽,并结合形状特征剔除错误目标。对于白色安全帽,将原始图像转化成B通道下的灰度图像,解决了将黄色误检为白色的问题。采用V通道直方图均衡化的方法,提升了昏暗光线条件下的图像亮度。实验结果表明:在无需提前训练的情况下,算法对于单色安全帽识别准确率超过了91%,对于多色安全帽识别率超过了90%,为施工危险区域的安全隐患排查和作业管控提供了解决方案。

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

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。

基于Raspberry Pi的实时流媒体直播系统设计

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.

Multi-Plate Microbial Monitoring Terminal Based on Raspberry Pi 4B

We utilized Raspberry Pi 4B to develop a microbial monitoring system to simplify the microbial image-capturing process and facilitate the informatization of microbial observation results.The Raspberry Pi 4B firmware,developed under Python on the Linux platform,achieves sum verification of serial data,file upload based on TCP protocol,control of sequence light source and light valve,real-time self-test based on multithreading,and an experiment-oriented file management method.The system demonstrated improved code logic,scheduling,exception handling,and code readability.

A Deep Trash Classification Model on Raspberry Pi 4

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.

Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

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.

Securing Transmitted Color Images Using Zero Watermarking and Advanced Encryption Standard on Raspberry Pi

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.

Design of Li River Water Quality Dynamic Monitoring System Based on Raspberry Pi and TinyML

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.

基于Raspberry Pi开发板的智能通风检测系统设计

采用Raspberry Pi开发板的智能通风检测系统的控制和数据处理为核心,与显示器、温湿度传感器、火焰传感器和通风模块等互联互通,完成智能通风检测系统硬件、软件设计,实现大棚内智能化温湿度调控。基于云平台完成数据在PC端和手机端无线远程控制并实时显示,模拟温室大棚的环境,并验证系统的安全可靠性,该系统具有经济造价低、安全可靠、控制简单、易于推广等特点。

基于Raspberry Pi的物联网智能宠物看护系统

目前,宠物狗看护系统的最大问题在于视觉盲点和无法感知宠物狗生理状态。利用Raspberry Pi优越的性能进行宠物狗监控系统的设计,系统包括多个传感器和智能摄像头来综合监测宠物狗的运动轨迹和生存状态,达到无人看护的目的。实验验证,系统实现了较为经济高效的宠物狗无人看护功能,用户可轻松进行宠物狗无人管理。

基于Raspberry Pi的智能蜂箱系统设计

传统的蜂养殖大量依赖于人的现场监管,对蜂群状态、蜂箱内部的环境状况都需要进行实地考察和开箱检测,这种“入侵式”的监控方式不仅费时费力,并且存有安全风险。如何尽量减少养殖者的现场监管,在降低成本和安全风险的同时提高养殖效率,成为近几年蜂场进一步发展所面临的问题之一。为解决上述问题,借助Raspberry Pi及相关传感器和控制器,设计和搭建了一个物联网智能蜂箱系统。系统提供了手动和自动控制功能,能有效对蜂箱内环境进行远程监控,并具有响应时间短、可靠性高等优点,可以有效减少蜂养殖者的劳动强度并提高养殖效率。

基于Raspberry Pi和Node.js的北斗RTK物联网设备开发

利用低成本硬件终端,开发了一套高精度北斗RTK物联网设备。软件运行平台为搭载Raspbian系统的Raspberry Pi4B,通过RTKLIB-str2str实现北斗差分数据流,利用Node.js将高精度RTK定位结果上传至阿里云物联网平台并存储。该设备可结合GIS开发实现高效可视化和空间分析,在多个行业中智能化应用场景。

基于Raspberry Pi的声源定位跟踪系统

研究设计介绍了以Raspberry Pi开发板为核心处理器,配备麦克风阵列,开发版为Arduino板等组成的一个声源定位跟踪系统。系统基本工作流程为:由麦克风阵列拾取语音信号,继而通过Raspberry Pi开发版对信号的处理,其中信号处理方法包括FFT算法滤波降噪以便更精准的接收声源,再通过TDOA算法得到时差数据信息,进一步确定声源位置,获取位置后通过串口传输给二维云台,二维云台承载激光笔转向至声源处并开启激光指向声源,当声源移动时,云台实现动态追踪。其次,基于到达时间延时的定位算法很多,改进了一种的互功率谱相位加权算法。该算法运算量少,经实验证明,在强噪声环境下该算法具有较好的定位性能。

一款基于Raspberry Pi的云智能中药煎药机设计

文中设计一款基于Raspberry Pi 4主控器的云智能中药煎药机。其通过物联网技术,使用OneNET云平台作为数据交换中转,开发相应的APP,实现云智能煎药机与手机端连接;使用云智能实验平台,设计基于模糊PID控制算法的温度控制系统,最终实现了在无人看守的环境下能够自动下药、自动供水、自动吸取药液、保温、远程操作等功能。

基于Raspberry Pi的消防员定位系统设计

采取案例分析法,归纳消防员参与大型火场扑救伤亡案情,在查阅文献资料的基础上进一步总结消防员伤亡原因,分析消防员定位系统存在的问题,并结合当前Raspberry Pi的卡片机优势,有针对性地设计Raspberry Pi消防员定位系统所需的模块和实现的功能。

基于Raspberry Pi的六足行走机器人设计

为在各种人类不便到达的地区进行数据采集、探测等工作,越来越多移动式机器人被使用,六足机器人因其在步态上的高适应性成为移动机器人领域中的主流研究对象之一。本文以Raspberry Pi作为控制核心设计了一款六足行走机器人,具有多自由度结构和离散落足点,可进行丰富的步态设计,以适应各种非结构化地形,同时结合摄像头、传感器等功能模块,使其具备远程控制、实时监控、自动避障等功能,以达到在各种复杂地形环境下稳定行走并实时高效采集信息的目的。

基于Raspberry Pi的潮汐车道智能调节系统

本文设计了一套基于Raspberry Pi的潮汐车道智能调节系统。由车流量监测模块、环境感知模块、供电模块、处理控制模块、机械运动模块、无线通讯模块、警示预告模块等模块组成,针对传统潮汐车道固定的车道变更时间节点与使用时段、人工搬挪隔离栅栏严重制约潮汐车道使用效率与安全保障的问题,通过硬件模块监测车辆行驶与道路交通量信息,实现潮汐车道适时自动调节与换道自动告警,进而提高潮汐车道的使用效率与安全保障。

基于Raspberry Pi的户外广告牌防倾倒系统设计

为有效监控户外广告牌的安全状态,降低牌体倾倒坠落的概率,采用树莓派系统依托物联网、无线传感技术开发了一套状态检测报警系统。该系统由数据采集层、智能预警层、信息传输层三层构成,不仅通过声光信号的多重配合实现了部分故障隐患自我排查与显示,还通过Socket通讯技术利用局域网实现了服务器与客户端之间的通信,有效提高了监管维修的效率。具有很大的灵活性和扩展性。

基于RTKLIB和Raspberry Pi的低价RTK设备

在Raspberry Pi 3上运行开源RTK软件包RTKLIB,再结合u-blox NEO-M8T,自制一个低价RTK设备。实际测试结果表明,低价RTK设备定位能达到dm级别,在环境较好的地方,RTK固定解后精度可达cm级别。该设备可广泛运用于地质、物探以及部分测绘领域。

基于Raspberry Pi的敲击乐队设计与实现

随着现代社会的快速发展,人们工作愈发繁忙,生活愈发疲惫,心理压力急剧增加,因而对精神方面的娱乐享受需求也愈发强烈。研究表明,在日常生活中增加一些轻松的娱乐活动或欣赏舒缓优美的音乐,都可在一定程度上减少压力积累和负面情绪。以现代人的休闲娱乐需求为出发点,设计了一种小型有趣的敲击乐队机器人。该装置以Raspberry Pi为控制核心,通过对多路继电器的控制来实现电磁铁的运动,从而完成对各个乐器的开合、力度等控制;具有按键和Web双控制模式,能带来更好的使用体验。该敲击乐队的设计具有较好的应用前景。