Camera Module Research Articles

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.

Real-time vehicular accident prevention system using deep learning architecture

Approximately 20 million people die every year of road accidents, mainly caused due to ignorance of road safety norms and traffic rules. Drivers’ experience and prudence still have to be relied upon to prevent vehicle accidents. Here, an algorithm is developed to prevent T-bone accidents, rear-end and head-on collisions, pedestrians accidents. Using Raspberry Pi 4 Model B with 8 GB RAM as processing unit, importing OpenCV library into python 3; an intensive dynamic real-time algorithm for the effective rear end and adjacent collision avoidance module is developed on the framework of deep learning architecture(DLA) for image categorization. In this work, real-time object identification, distance estimation, and tracking of the instantaneous position are performed in all environmental conditions using a deep learning algorithm without any additional sensors. The Raspberry Pi NoIR Camera Module V2 keeps sending the captured frames in real-time to feed the MobileNet DLA for Single Shot Multibox Detection (SSD). The attributes of the preceding obstructions are utilized in such a way that the live-distance between vehicles and other objects is approximated successfully by providing the instantaneous positional information whereby generating warning signal on exigency with a processing speed of 0.09 s per frame (11 fps). The real-time output data are analyzed by multivariate analysis and a 3x3x9 repeated measures ANOVA. The result shows that Multimodal Early Accident Alarming System is highly effective for distractive drivers.

Smart Temperature and Mask detection System using Raspberry Pi and Temperature Sensor with Camera and Barrier Enabled

The COVID-19 pandemic, which is caused by a new corona virus, is still spreading. More than sixteen million individuals have been infected across India so far, and the figure is continually rising. The health-care system is in critical condition. Wearing a mask is one of the many preventive steps adopted to decrease the spread of the disease. It's tough to physically check if someone is wearing their facial mask appropriately and to assess their temperature. Early symptom detection and good hygiene standards are therefore critical, particularly in situations where individuals come into random touch. In this research, we propose a Raspberry Pi-based system for limiting COVID-19 development by detecting and monitoring persons who are not wearing a face mask. Unlike handheld thermometers, which require a person to take a person's body temperature, our system uses the IR Thermal temperature sensor to provide a contactless and efficient temperature check, and an OpenCV subsystem based on a Raspberry Pi single board computer equipped with a camera module to allow or deny a person's entry into buildings and event venues for facial mask detection. Keywords: OpenCV, Python, Tensor Flow, Raspberry pi, Keras, Deep learning, COVID-19.

A Study on the Detent Torque and Holding Torque of a Micro-Claw Pole Stepper Motor.

The micro-claw pole stepper motor is widely used in the field of camera modules and VR focusing. The influence of torque ripple on positioning accuracy becomes more obvious with a decrease in motor volume. In order to reduce the torque ripple of the micro-claw stepper motor and increase the load capacity of the motor, the torque of the motor is simulated by using finite element software. Firstly, the influences of four parameters, namely air gap, magnet thickness, claw thickness and claw height, on the detent torque and holding torque of the claw permanent magnet stepper motor are obtained through the Taguchi experiment. The Signal-to-noise ratio (SNR) of each factor to the response was calculated and the degree of influence of the four parameters on the detent torque and holding torque of the micro-claw pole permanent magnet stepper motor was determined. Then, the optimal value of each factor to reduce the detent torque and increase the holding torque was obtained through optimization analysis. Finally, experiments were carried out to test the holding torque of the motor, and the accuracy of the results was verified by comparing the test values with the simulation values. According to the analysis of the paper, the response delta of air gap to detent torque is the largest, reaching 5.99, and that to holding torque is 0.73. The response delta of the magnet thickness to the detent torque is 5.87, and the response delta to the holding torque is 1.52. The optimized parameters obtained by optimization analysis reduce the detent torque of the motor by 26.74% and increase the holding torque by 18.35%. It is found that air gap and permanent magnet thickness have the greatest influence on the detent torque and holding torque of a micro-claw permanent magnet stepper motor, followed by claw thickness and claw height. Among them, the air gap has more influence on the detent torque than on the holding torque, and the thickness of the permanent magnet has more influence on the holding torque than on the detent torque.

Attendance management using face recognition and fingerprint

In this era of technology where everything is getting automated, we are still relying on the old method of using pen and registers for attendance. This project aims to automate the attendance process using biometrics. In this project, Face Recognition and fingerprint have been used to mark attendance of students. This is implemented using Arduino UNO and Raspberry pi as the controllers. A fingerprint sensor is connected with Arduino and camera module with Raspberry pi. First, finger is scanned and then face is recognised with with the help of database. Final attendance is accessed via the internet by the authorized personnel.

Design and Implementation of a Car’s Black Box System Using a Raspberry Pi and a 4G Module

The design and implementation of a car’s black box system using a Raspberry Pi microcomputer and an Internet of things module is presented in this research. This system was built using a Raspberry Pi microcomputer and different sensors, including a GPS, camera module, audio module, alcohol sensor module, and signals from the electronic control unit. The data were stored in both a secure digital card and in the cloud using the Waveshare SIM7600G-H 4G module. The results show that this embedded system can acquire and process video, audio, GPS data, alcohol concentration, speed, temperature, etc. Finally, a graphics user interface was developed to process the stored files. This system is similar to a black box in airplanes, which records all flight information into the black box using a specific algorithm. This makes the recorded flight data accessible to authorities when needed.

DEVELOPING DESIGN OF AUTOMATIC EGG QUALITY DETECTOR USING ROI AND RGB TEMPLATE METHODS

The eggs of purebred chickens always become the major choice for satisfying people’s needs for nutrients. The way to process them is easy, and the prices are relatively cheap for all people. Since the eggs of purebred chickens are in high demand, a device that can detect their qualities more correctly and accurately is needed, compared to the conventional method. Objective: An automatic egg quality detector was developed through a camera module of pixy cmucam5 by employing ROI and template RGB methods. The implementation of the ROI method could optimize the system performance, thereby accelerating the process of egg quality detection carried out by the preprocessor of the pixy camera. ROI analyzed the egg image only on the desired pixel area, not the whole pixel. As a result, the ROI method yielded a device and system that could detect the egg quality within the average speed of detection time -/+ 2ms per egg out of 4 trial categories. Meanwhile, the template RGB method was able to improve and ease the ability of the pixy camera module in detecting the egg quality. When the RGB index of the egg was < 234, then the egg was of good quality. Contrarily, when the RGB index of the egg was > 234, the egg quality was in a bad category. The contribution of this paper is to develop a low-cost automatic egg sorting and detection system with the advantages of flexibility in device placement, due to the use of camera modules and object detection accuracy, as well as better response time than using color sensors. Accordingly, the template RGB method produced a device and system which could detect egg quality with average accuracy of 90 % and average error of 10% out of 4 trial categories. And, the results of the trials upon the detector system indicated success in detecting the egg quality.

Design and Characterization of an Optically Segmented Single Volume Scatter Camera Module

The Optically Segmented Single Volume Scatter Camera (OS-SVSC) aims to image neutron sources for nuclear non-proliferation applications using the kinematic reconstruction of elastic double-scatter events. We report on the design, construction, and calibration of one module of a new prototype. The module includes 16 EJ-204 organic plastic scintillating bars individually wrapped in Teflon tape, each measuring 0.5 cm$\times$0.5 cm$\times$20 cm. The scintillator array is coupled to two custom Silicon Photomultiplier (SiPM) boards consisting of a 2$\times$8 array of SensL J-Series-60035 Silicon Photomultipliers, which are read out by a custom 16 channel DRS-4 based digitizer board. The electrical crosstalk between SiPMs within the electronics chain is measured as 0.76% $\pm$ 0.11% among all 16 channels. We report the detector response of one module including interaction position, time, and energy, using two different optical coupling materials: EJ-560 silicone rubber optical coupling pads and EJ-550 optical coupling grease. We present results in terms of the overall mean and standard deviation of the z-position reconstruction and interaction time resolutions for all 16 bars in the module. We observed the z-position resolution for gamma interactions in the 0.3 MeVee to 0.4 MeVee range to be 2.24 cm$\pm$1.10 cm and 1.45 cm$\pm$0.19 cm for silicone optical coupling pad and optical grease, respectively. The observed interaction time resolution is 265 ps$\pm$29 ps and 235 ps$\pm$10 ps for silicone optical coupling pad and optical grease, respectively.

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.

Bio-Medical Waste Management System

Abstract: Sorting of products is a very difficult industrial process. Continuous manual sorting creates consistency issues. This paper describes a working prototype designed for automatic sorting of objects based on the colour. We are using Pi camera to detect the object and the Raspberry Pi microcontroller was used to control the overall process. The identification of the biomedical Waste is based on the image processing using Pi camera module. Two conveyor belts were used, each controlled by separate Servo motors. The first belt is for placing the product to be analysed by the pi camera module, and the second belt is for moving the container, having separated compartments, in order to separate the products. The experimental results promise that the prototype will fulfil the needs for higher production and precise quality in the field of automation. Keywords: Raspberry pi, Servo Motor, Pi camera, Programming

Mobile Application to Address Civic Issues

This programme allows users who are experiencing troubles as a result of a civic issue such as a rubbish dump, sewage leak, or potholes, to file a complaint using photographs (Camera Module). The public can be notified of the resolved issue (through an image or message) by the relevant government agency. This software provides a transparent platform for customers to register complaints, view previous complaints, and validate that the problem has been resolved. So, the proposed system would be that people could take pictures of neighbourhood issues and submit them to a local government body, where the complaint would be lodged along with the issue's address, and the local government would then forward the problem to the appropriate department, which would handle the issue. Citizens will be informed of the current status of the complaint, as well as what actions are being done to address it and how quickly the problem can be resolved.

Multiagent off-screen behavior prediction in football

In multiagent worlds, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment. These interactions, combined with the potential stochasticity of the agents’ dynamic behaviors, make such systems complex and interesting to study from a decision-making perspective. Significant research has been conducted on learning models for forward-direction estimation of agent behaviors, for example, pedestrian predictions used for collision-avoidance in self-driving cars. In many settings, only sporadic observations of agents may be available in a given trajectory sequence. In football, subsets of players may come in and out of view of broadcast video footage, while unobserved players continue to interact off-screen. In this paper, we study the problem of multiagent time-series imputation in the context of human football play, where available past and future observations of subsets of agents are used to estimate missing observations for other agents. Our approach, called the Graph Imputer, uses past and future information in combination with graph networks and variational autoencoders to enable learning of a distribution of imputed trajectories. We demonstrate our approach on multiagent settings involving players that are partially-observable, using the Graph Imputer to predict the behaviors of off-screen players. To quantitatively evaluate the approach, we conduct experiments on football matches with ground truth trajectory data, using a camera module to simulate the off-screen player state estimation setting. We subsequently use our approach for downstream football analytics under partial observability using the well-established framework of pitch control, which traditionally relies on fully observed data. We illustrate that our method outperforms several state-of-the-art approaches, including those hand-crafted for football, across all considered metrics.

PyZebrascope: An Open-Source Platform for Brain-Wide Neural Activity Imaging in Zebrafish.

Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors.

Alert System for Driver’s Drowsiness

Monotonous driving for longer distances results in drowsiness and mental fatigue. Consumption of alcohol can also make the driver feel sleepy. Thus, drowsiness and drunken driving cause road accidents. This paper proposes a real-time detection of drivers’ drowsiness and subsequently alerting them. The main aim of this proposed system is to reduce the number of accidents due to driver’s Drowsiness and alcohol intake to increase transportation safety. The proposed system contains the use of the Raspberry Pi system and Pi camera module for drowsiness detection of the driver and alerting the driver using an alarm system.

Improving Lane Detection Performance for Autonomous Vehicle Integrating Camera with Dual Light Sensors

Automotive companies have studied the development of lane support systems in order to secure the Euro New Car Assessment Program (NCAP)’s high score. A front camera module is applied with safety assistance systems in an intelligent vehicle. However, the front camera module has limitations in terms of backlight conditions, entering or exiting tunnels, and night driving because of lower image quality. In this paper, we propose an integrated camera with dual light sensor for improving lane detection performance under the worst conditions. We include a new algorithm to enhance image data quality and improve edge detection and lane tracking using illumination information. We evaluate the tests under various conditions on a real road. These tests are performed on 728 km of road (under various external situations and lane types) for false alarm rates. The experimental results show that the system is promising in terms of reliability, enhancement, and improvements.

Digital Forensics based Defence Security System

Abstract: This project addresses the structure, implementation and application of digital forensics security System with the assistance of face recognition and fingerprint identification which might be employed in Military Bases and other defence bases. Forensics munition using face and fingerprint recognition may be a system during which tracking of daily activity of officers is in an automatic manner and done by the program itself which is finished by the popularity and identification of the face with the assistance of a camera module which is connected with the system. The camera module helps the system with the initial a part of the program which is initialization of the camera on which the system can perform its operation. When the face comes before of the camera and provides their fingerprints, the program compares the face within the camera with the photographs and detect the fingerprint within the database and recognizes it. These image data are basically known data for the system and is that the data on which the system is trained. The system, if recognizes the face after the comparison with the database, displays the name of the person’s face as an alternative displays “UNKNOWN” if the faces don’t match. Then it tracks the identity of the person file of only the matched faces. This data is stored in an excel file format safely

Internet of things module accelerated dense deep learning for crime detection in surveillance systems

The smart surveillance system is becoming a vital application in each streets or houses. Most of the streets are prone to several misbehavior conducts for instance theft in atm, robbery, fights etc. and hence it is necessary to detect and analyse the crime scenes for finding the suspects. However, most of the surveillance system suffers from poor detection of objects due to poor camera resolution, absence of light and other factors. In order to improve the detection of faces after detecting the objects using ResNet, it is necessary to adapt some advanced devices for image capturing and analyzing. In this paper, Internet of Things (IoT) based ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Module 2MP is used for image acquisition that capture better images from the scenes. The study uses dense convolutional network namely DenseNet to detect the faces present in the crime scenes after the object detection. The deep learning module is trained with selected crime scenes for training the classifier. The simulation is conducted further to validate the model with other variants of deep learning.

The Child and Women Safety with Wearable Devices

In today's environment, the safety of children and women is becoming increasingly crucial. Women's crime is rising day by day. To address this problem, a technology-driven solution based on the most recent technical advances can be devised. This is an IoT-based solution that will assist us in effectively combating the conditions. Camera, GPS, GSM, microphone, and voice recognition modules are all hidden inside. The voice command will send an SOS signal to the people on your contact list as well as local authorities. Authorities can rush to the scene of the occurrence and stop it from happening. Immediately when the instruction through voice is turned on, the camera begins recording, and uploading the data to cloud storage. The recordings from the camera will be utilized for finding the criminal.

Development of Computer Vision Algorithms for Multi-class Waste Segregation and Their Analysis

Classification of waste for recycling has been a focal point for scientists interested in the field of conservation of the environment. Recycling consists of numerous steps, of which one of the most crucial is the segregation of recyclables from all other waste. Due to a lack of safety standards in developing countries, waste collection is often done manually by domestic helpers, or "rag-pickers". Such a process risks individual and public health. The waste collection methods may ultimately cause waste to become non-recyclable due to cross-contamination. Literature shows that research in this direction focuses on a single class of waste detection. The proposed work investigates CNN, YOLO, and faster RCNN-based multi-class classification methods to detect different types of waste at the collecting point. The smart dustbin proposed employs these computer vision methods with a Raspberry Pi microcontroller and camera module. The experimental results for multi-class classification show that the CNN has 80% of accuracy with 60% of the loss. Whereas the YOLO algorithm shows an accuracy of 88% and a loss of 40%. But the best results were obtained from faster RCNN object detection with API, with an accuracy of 91% and a loss of 16%. There is already an existing method for making a smart dustbin, so the results are compared to show how computer vision can be used to make a smart dustbin. This shows how computer vision can be used to make a smart dustbin. Doi: 10.28991/ESJ-2022-06-03-015 Full Text: PDF

An infrared image-enhancement algorithm in simulated prosthetic vision: Enlarging working environment of future retinal prostheses.

Most existing retinal prostheses contain a built-in visible-light camera module that captures images of the surrounding environment. Thus, in case of insufficient or lack of visible light, the camera fails to work, and the retinal prostheses enter a dormant or "OFF" state. A simple and effective solution is replacing the visible-light camera with a dual-mode camera. The present research aimed to achieve two main purposes: (1) to explore whether the dual-mode camera in prosthesis recipients works under no visible-light conditions and (2) to assess its performance. To accomplish these aims, we enrolled subjects in a psychophysical experiment under simulated prosthetic vision conditions. We found that the subjects could complete some simple visual tasks, but the recognition performance under the infrared mode was significantly inferior to that under the visible-light mode. These results inspired us to develop and propose a feasible infrared image-enhancement processing algorithm. Another psychophysical experiment was performed to verify the feasibility of the algorithm. The obtained results showed that the average efficiency of the subjects completing visual tasks using our enhancement algorithm (0.014 ± 0.001) was significantly higher (p < 0.001) than that of subjects using direct pixelization (0.007 ± 0.001). We concluded that a dual-mode camera could be a feasible solution to improving the performance of retinal prostheses as the camera adapted better to the specific existing ambient light conditions. Dual-mode cameras combined with this infrared image-enhancement algorithm could provide a promising direction for the design of future retinal prostheses.