Raspberry Pi Camera Module Research Articles

YOLOv3 Based Smart Assistive Navigation System for Visually Impaired Individuals

Abstract: Nowadays, some people suffer from blindness or vision impairment, either from childhood or due to other causes. These individuals frequently face difficulty to move from source to destination precisely, resulting in difficulties in their daily routines. Currently, people are using normal walking sticks, that is not useful for all the time, especially in outdoor environments, resulting in accidents and other difficulties. This paper aims to focus on improving the quality of life for blind people or visually impaired individuals by providing a suitable solution to enhance navigation and manage their needs in indoor and outdoor environments through the use of wearable devices. One proposed solution involves the method of voice assistant technology, which allows users to easily communicate and receive clear instructions. The Raspberry Pi camera module, that allows to capture images and video streaming with the Raspberry Pi, where the Ultrasonic sensor is commonly used for distance measurement to detect obstacles. Furthermore, incorporating a Neo 6m GPS module offers precise location data and navigation guidance, aiding users in reaching their destination safely and efficiently.

Arecanut Status Detection Using Deep Learning

Arecanut, a significant cash crop in many tropical regions, undergoes distinct stages of ripening, posing challenges for timely harvest and market readiness. This research presents a comprehensive framework employing cutting-edge deep learning methodologies, specifically TensorFlow Lite, for accurate and real-time detection of Arecanut status, encompassing ripe, unripe, and dry stages. The integration of OpenCV for image preprocessing and deployment on Raspberry Pi enhances the system's accessibility and usability, enabling on-site detection using the Raspberry Pi camera module. The study begins with a comparative analysis between traditional machine learning techniques, particularly Support Vector Machine (SVM), and deep learning architectures. While SVM exhibited commendable accuracy at 75%, the superior performance and scalability of deep learning models motivated the adoption of TensorFlow Lite. Extensive experimentation, conducted on a diverse dataset of Arecanut images, underscored the efficacy of deep learning in surpassing the accuracy thresholds achieved by conventional approaches. The synergy between TensorFlow Lite and Raspberry Pi leverages the computational capabilities of the latter, facilitating efficient deployment in resource-constrained agricultural settings. This synergy enables real-time inference directly on the device, ensuring data privacy, low latency, and autonomy from cloud connectivity, thus empowering farmers with actionable insights at the point of need, distinguishing between ripe, unripe, and dry stages. The experimental validation of our approach across varied environmental conditions underscores its robustness and adaptability. The deployed system not only offers a portable and cost-effective solution but also contributes to the paradigm shift towards precision agriculture, where datadriven decision-making enhances productivity and sustainability.

Optimization of Elevator Usage by Image Processing for Optimal Energy Conservation

An efficient vertical transit system is a crucial feature of contemporary office buildings. Modern machine learning (ML) algorithms make it simple to optimise lift control strategies. This study proposes a revolutionary way to use Raspberry Pi camera module data for the best possible dispatching of traditional passenger lifts. It is assumed that an image processing system processes a real-time video to ascertain the quantity of people and objects utilising the lifts and waiting for a lift vehicle in the halls. These numbers are assumed to be connected to a specific uncertain probability. The efficiency of our unique lift control algorithm is derived from the need to serve a crowded floor completely, sending as many lifts as possible there and filling them to the maximum weight permitted, in addition to the probabilistic utilisation of the number of people and/or items waiting. The suggested technique introduces the idea of the effective number of persons and items to account for the uncertainty that may arise from the image processing system's imperfection. Reducing wait time, energy conservation and optimization are main the goals of this research. The proposed approach was implemented, and the simulation results showed that the passenger journey time was reduced. A three-story office building was the intended application for the prototype model. Key Words: Elevator, Raspberry Pi, Machine Learning, Optimization, Image Processing

Envisioning Sight Beyond Sight: Revolutionizing Visionary Assistive Smart Glasses for Visually Challenged

This paper presents a groundbreaking development in assistive innovation through the improvement of visionary smart glasses custom-made for people with visual impedances. These smart glasses are equipped with a Raspberry Pi Camera module designed to capture images of printed materials such as daily papers, books, and other objects of interest. The captured visual information is then handled through advanced optical character recognition (OCR) technology, changing over it into text. Hence, the text information is consistently transformed into audible content using text-to-speech (TTS), which the user can tune in to through an associated headset.The main objective of these visionary smart glasses is to enable the visually challenged by giving real-time access to printed materials and improving their ability to independently engage with composed data. Furthermore, these smart glasses incorporate a second module outlined for cash reading, permitting users to precisely distinguish and manage distinctive categories of currency. This dual-functionality aims to extend availability and independence, subsequently cultivating more prominent inclusivity and quality of life for individuals with visual impairments.The advancement of these visionary smart glasses represents a critical step towards bridging the data gap that frequently hinders the visually challenged community. By consistently converting printed materials into audio and empowering quick and reliable cash identification, these inventive smart glasses are poised to revolutionise the way people with visual disabilities interact with their environment, eventually enhancing their freedom and overall well-being. Keywords— Envisioning, Revolutionizing, Raspberry Pi, Camera Module, OCR, TTS, Currency.

Weed detection and removal using robotic system

Weeding is one of the most significant practices in agricultural production. Weeds are unwanted plants that grow along with the crops and compete with the crops for space, light, water, and soil nutrients. Weeds propagate themselves either through seeding or creeping rootstalk and decrease yields, increase production costs, interfere with the harvest, and lower the product quality. The use of herbicides reduces labor requirements for weed control by up to 60 percent but affects environmental quality and can be toxic to a wide range of organisms. Hence it is necessary to develop an automated system to identify and remove weeds from the vegetable fields. The objective of the proposed work is to develop a mobility level tracked bot that identifies the weeds and removes them with the help of a robotic end effector and to develop a machine learning model to identify the weeds. This functional module will be processed in a Raspberry Pi processor and by using a Raspberry Pi camera module the bot will detect the weeds in vegetable fields. We performed weed detection with different machine learning models like Haar cascade, YOLOv5, and CNN. To evaluate the performance of the machine learning models used, the performance metrics accuracy, precision, recall, and F-measure are estimated and it has been found that CNN has better accuracy, precision, and recall as compared to YOLOv5 and Haar cascade. CNN has the highest F-measure among the three algorithms at 98%. The weed removal is done using a robotic end-effector which is controlled by the Arduino UNO based on the signal from Raspberry Pi.

High-Throughput Plant Phenotyping System Using a Low-Cost Camera Network for Plant Factory

Plant phenotyping has been widely studied as an effective and powerful tool for analyzing crop status and growth. However, the traditional phenotyping (i.e., manual) is time-consuming and laborious, and the various types of growing structures and limited room for systems hinder phenotyping on a large and high-throughput scale. In this study, a low-cost high-throughput phenotyping system that can be flexibly applied to diverse structures of growing beds with reliable spatial–temporal continuities was developed. The phenotyping system was composed of a low-cost phenotype sensor network with an integrated Raspberry Pi board and camera module. With the distributed camera sensors, the system can provide crop imagery information over the entire growing bed in real time. Furthermore, the modularized image-processing architecture supports the investigation of several phenotypic indices. The feasibility of the system was evaluated for Batavia lettuce grown under different light periods in a container-type plant factory. For the growing lettuces under different light periods, crop characteristics such as fresh weight, leaf length, leaf width, and leaf number were manually measured and compared with the phenotypic indices from the system. From the results, the system showed varying phenotypic features of lettuce for the entire growing period. In addition, the varied growth curves according to the different positions and light conditions confirmed that the developed system has potential to achieve many plant phenotypic scenarios at low cost and with spatial versatility. As such, it serves as a valuable development tool for researchers and cultivators interested in phenotyping.

Automatic Attendance System Using Face Recognition

The project aims to develop an automated attendance system using face recognition technology. The system eliminates manual methods and provides contactless attendance tracking. It utilizes a Raspberry Pi board, camera module, and advanced image processing techniques to detect and recognize faces in real-time. Attendance data is stored in a database which is excel sheet and can be accessed remotely. The system improves efficiency, reduces errors, and promotes hygiene and safety in various environments.

Implementasi Sensor PIR Dan Kamera Untuk Keamanan Ruangan Berbasis Internet of Things

Room security is a very important thing to prioritize. One of the solutions to prevent crime levels needs to be equipped with a security device or a device capable of monitoring a room with the ability to detect the presence of an unwanted person. Therefore, this study designed a tool to monitor rooms based on the Internet of Thing (IoT) with surveillance cameras, which is one way to monitor the situation in a room. This system combines the use of the pir sensor, the raspberry pi camera module, and the raspberry pi, in order to be able to find out the condition of the room in the form of photos sent and also use bot features on internet applications, namely telegram messenger. This system is more efficient because it uses storage from telegram, and also the system will send notifications via telegram bots. So that the user can take action as soon as possible if things happen that are not desired. When the pir sensor detects movement in the room, the camera takes a photo and then sends it to the user's telegram bot account and the buzzer sounds as an early prevention of crime.

A Robotized Raspberry-Based System for Pothole 3D Reconstruction and Mapping.

Repairing potholes is a task for municipalities to prevent serious road user injuries and vehicle damage. This study presents a low-cost, high-performance pothole monitoring system to maintain urban roads. The authors developed a methodology based on photogrammetry techniques to predict the pothole's shape and volume. A collection of overlapping 2D images shot by a Raspberry Pi Camera Module 3 connected to a Raspberry Pi 4 Model B has been used to create a pothole 3D model. The Raspberry-based configuration has been mounted on an autonomous and remote-controlled robot (developed in the InfraROB European project) to reduce workers' exposure to live traffic in survey activities and automate the process. The outputs of photogrammetry processing software have been validated through laboratory tests set as ground truth; the trial has been conducted on a tile made of asphalt mixture, reproducing a real pothole. Global Positioning System (GPS) and Geographical Information System (GIS) technologies allowed visualising potholes on a map with information about their centre, volume, backfill material, and an associated image. Ten on-site tests validated that the system works in an uncontrolled environment and not only in the laboratory. The results showed that the system is a valuable tool for monitoring road potholes taking into account construction workers' and road users' health and safety.

Development of a Low-Cost Luminance Imaging Device with Minimal Equipment Calibration Procedures for Absolute and Relative Luminance

Luminance maps are information-dense measurements that can be used to directly evaluate and derive a number of important lighting measures, and improve lighting design and practices. However, cost barriers have limited the uptake of luminance imaging devices. This study presents a low-cost custom luminance imaging device developed from a Raspberry Pi microcomputer and camera module; however, the work may be extended to other low-cost imaging devices. Two calibration procedures for absolute and relative luminance are presented, which require minimal equipment. To remove calibration equipment limitations, novel procedures were developed to characterize sensor linearity and vignetting, where the accurate characterization of sensor linearity allows the use of lower-cost and highly non-linear sensors. Overall, the resultant device has an average absolute luminance error of 6.4% and an average relative luminance error of 6.2%. The device has comparable accuracy and performance to other custom devices, which use higher-cost technologies and more expensive calibration equipment, and significantly reduces the cost barrier for luminance imaging and the better lighting it enables.

Real Time Object Detection Using Raspberry Pi

Abstract: Due to recent advances in deep learning, the performance of object detection techniques has greatly increased in both speed and accuracy. This enabled highly accurate real-time object detection in modern desktop systems. This project investigates the applicability of working object detection on Raspberry Pi 3. Real-time detection of objects requires a lot of processing power, and achieving real-time speed is a difficult task in a system with limited performance. Many different methods can be used to detect objects. Two methods were implemented in the Raspberry Pi 3 B to determine if they are suitable to work with such weak hardware. An implemented target detector is considered suitable if it achieves a high enough resolution and frame rate to be useful in practical applications. The evaluation performs a number of tests on each detector and measures their performance in terms of detection accuracy, hell time, and frame rate. Raspberry Pi Model B, which is the latest and most powerful product of the Raspberry Pi series, is used as hardware. The camera used is a Raspberry Pi Camera Module v2

Design and Implementation of a Smart Surveillance Security System

Home security is essential for occupant’s conveniences and protection. This research project designed and implemented a comparatively inexpensive smart surveillance security system that automatically captures an intruder’s image through a raspberry pi camera module and PIR sensor and sends mail to the user via Wi-Fi using the users registered email address. This system operates by triggering the Pi Camera through Raspberry Pi whenever an intruder comes in range using PIR sensor. The Pi camera will capture the image, save it and send the image of the intruder via mail to the user through the help of the command codes embedded in the Raspberry pi microcontroller. This research project will enable home/supermarket and office owners to secure their facilities and monitor the activities of their employers at any location at cheaper cost which is the earnest desire of an Engineer. Proteus 2022 was used as the simulation tool. Keywords: Security, Spy camera, Raspberry pi, IOT, smart security system.

Evaluating Plant Drought Resistance with a Raspberry Pi and Time-lapse Photography.

Identifying genetic variations or treatments that confer greater resistance to drought is paramount to ensuring sustainable crop productivity. Accurate and reproducible measurement of drought stress symptoms can be achieved via automated, image-based phenotyping. Many phenotyping platforms are either cost-prohibitive, require specific technical expertise, or are simply more complex than necessary to effectively evaluate drought resistance. Certain mutations, allelic variations, or treatments result in plants that constitutively use less water. To accurately identify genetic differences or treatments that confer a drought phenotype, plants from all experimental groups must be subjected to equal levels of drought stress. This can be easily achieved by growing and imaging plants that are grown in the same pot. Here, we provide a detailed protocol to configure a Raspberry Pi computer and camera module to image seedlings of multiple genotypes growing in shared pots and to transfer images and metadata via the cloud for downstream analyses. Also detailed is a method to calculate percent soil water content of pots while being imaged to allow for comparison of stress symptoms with water availability. This protocol was recently used to uncouple differential water usage from drought resistance in a dwarf Arabidopsis thaliana mutant chiquita1-1/cost1 compared to the wild-type control. It is cost effective, suitable for any plant species, customizable to various biological questions, and requires no prior experience with electronics or basic software programming.

Smart system for early detection of agricultural plant diseases in the vegetation period

This paper presents a smart system for the early detection of agricultural plant diseases in the vegetation period. The proposed smart system allows the detection of three types of wheat diseases, particularly yellow rust, powdery mildew, and Septoria at an early stage, and significantly improves the soil and ecology by locally spraying harmful chemicals just on sick plants. The proposed disease-detecting method is based on the structure of a convolutional neural network (CNN) using the Pycharm program based on the C ++ programming language. The basic structure of the smart system consists of Raspberry PI 4 MODULE, Raspberry Pi camera module v2, buzzer, HC-SR04 distance sensor, rotor driver, AC motor, power supply, relay microelectronics, and some digital devices.

A low-cost and open-source measurement system to determine the Young’s and shear moduli and Poisson's ratio of soft materials using a Raspberry Pi camera module and 3D printed parts

Advances in biomedical and engineering fields have greatly increased the need for understanding of soft structures. Soft materials such as gelatin and gelatin-based hydrogels have grown in popularity for use in a wide variety of applications including tissue engineering, biofabrication, and organ transplantation. With hydrogel structures being used in such demanding applications, it is crucial to properly characterize the dynamic behavior of these soft structures. Although there have been major improvements in measurement technology for determining the mechanical properties of soft, translucent materials, it remains quite challenging to reliably measure the Young’s and shear moduli of these materials in a way that remains straightforward, low-cost, and non-contact. This research aims to address the weaknesses in modern measurement methods and develop a system suitable for characterizing the elastic moduli of soft materials that requires only four, inexpensive, off-the-shelf components. Utilizing a Raspberry Pi, stepping motor, and an inexpensive camera, the Young’s and shear moduli of a gelatin column is measured five times. The standard deviation between measurement was observed to be less than 0.15% with high accuracy having an error of less than 4.6% when compared to relatively expensive, conventional measurement techniques.

Analog Driven Robot using IoT and Machine Learning

Abstract: Visual impairment is one of the biggest limitations for humanity, especially in this day and age when information is communicated a lot by text messages (electronic and paper based) rather than voice. Facial recognition is category of biometric software that maps an individual’s facial features mathematically and stores the data as a sprint The software uses deep learning algorithms to compare a live capture or digital image to be stored face print in order to verify an individual’s identity vbnnn This project aims to develop a device to help people with visual impairment. In this project, we manufactured a device that converts an image’s text to speech. The basic outline is an embedded system that captures an image, extracts only the area of interest (i.e. region of the image that comprises text) and changes that text to speech. It is incorporated using a Raspberry Pi 3 and a Raspberry Pi camera module. We have two phases in our project that are Text to Speech and Facial Recognition. Every Module for picture handling and voice preparing are available in the device. It likewise can play and stop the output while reading. The expectation is that it has less error rate and less processing time and less cost productivity.

Vision to the Bi-Sionary

Abstract: Visual impairment is one of the biggest limitations for humanity, especially in this day and age when information is communicated a lot by text messages (electronic and paper based) rather than voice. Facial recognition is category of biometric software that maps an individual’s facial features mathematically and stores the data as a sprint The software uses deep learning algorithms to compare a live capture or digital image to be stored face print in order to verify an individual’s identity vbnnn This project aims to develop a device to help people with visual impairment. In this project, we manufactured a device that converts an image’s text to speech. The basic outline is an embedded system that captures an image, extracts only the area of interest (i.e. region of the image that comprises text) and changes that text to speech. It is incorporated using a Raspberry Pi 3 and a Raspberry Pi camera module. We have two phases in our project that are Text to Speech and Facial Recognition. Every Module for picture handling and voice preparing are available in the device. It likewise can play and stop the output while reading. The expectation is that it has less error rate and less processing time and less cost productivity.

Detection Malaria Base Microscope Digital Image with Convolutional Neural Network

Malaria is a tropical disease that infects human red blood cells caused by infection with the plasmodium parasite. Plasmodium parasites spread to humans through female Anopheles mosquitoes and can reproduce in human blood cells. Malaria is a health problem that is at risk of causing other health problems such as anemia and even death. The current gold standard for malaria diagnosis is laboratory diagnosis by microscopic examination to find the malaria parasite through the blood cells of the patient. However, the diagnosis of malaria through microscopic observation of blood cells has the potential to take a long time, because the plasmodium parasite has a very small size. The malaria detection system using the Convolutional Neural Network (CNN) method is designed to detect malaria in human blood cells. CNN is a machine learning method designed to classify objects in an image. The system was built in three stages of development, namely the development of a CNN model for malaria detection, software development and hardware development. The hardware components used in the system include Raspberry pi, Raspberry Pi camera module, and LCD. The results of the malaria detection test using the CNN model gave an accuracy of 98.76% which was tested on blood cell images from a microscope

Covid 19 Spread Prevention Based on IoT around ATM machines

COVID-19 has been discovered since 2019. It began in China and expanded over the world; its influence on countries was so severe that it affected the economics, politics, and others, as well as the lives of individuals in all facets of life. This illness is spreading so quickly that seven people out of every ten have it. To decrease losses in all aspects for the entire world, a device was designed to be inserted in ATMs via the Internet of Things, with the recommended approach detecting and preventing COVID-19 spread around ATMs. Picture processing algorithms are used to capture the person's image at the ATM, whether he is wearing a mask or not. If the individual is not wearing a mask, the system will enter a state of freeze and they will be unable to access it. It makes use of the Raspberry Pi and the Raspberry Pi Camera Module. Biometric sensors can scan the thickness of a person's finger, which is typically 4-6 mils thick. When inputting the pin code or using the ATM, the individual should wear gloves, keeping a social distance of about 2 meters (6 feet) with ATMs that use an ultrasonic sensor.

Human Image Classification and Identification

Abstract: Face Recognition is in recent times used as a chunk of various applications. It is used as a piece of the makes use of images processing applications. The Raspberry Pi 4 is embedded system. It is low cost, a single-board computer used to lessen the complexity of systems in actual time applications. This project is especially based on python. Raspberry pi carries Camera slot Interface (CSI) to interface the raspberry pi camera. Here, Images captured by using the Raspberry Pi camera module are improved so that it will identify the specific region of image. This is used in the real time application of ‘Smart Attendance System’. We have proposed to implement a “Smart attendance system using Face Recognition”. This system makes use of Face recognition package which saves a number of time than conventional method. This system requires Raspberry Pi 4, Open CV, Python etc. The system uses Face Recognition Package. This system compares the image of the test and the training image and decide who is present and absent. If student is absent a message will be sent to their parent’s phone number. Student will be greeted with the voice when he/she come in front of camera by using text to speech mechanism. The system wishes “Happy Birthday” if student’s birthday is there by checking the birth date and current date. Keywords: Open CV, Face Recognition, Raspberry Pi, Face recognition package.