Camera Module Research Articles

Vision-based approach for human motion detection and smart appliance control

This study focuses on the use of computer vision technology and motion detection sensors to create an intelligent system that recognizes human presence in monitored spaces. The system uses a relay module for automation and control of household appliances while sensing motion detection, operated by an ESP32 microcontroller. This innovative solution addresses two major issues in home automation: reliable human presence recognition and seamless appliance control. The research merges a camera-based vision system with motion sensors, comparing motion and vision-based identification. The ESP32 microcontroller improves motion detection precision and context awareness by integrating motion sensors and computer vision technologies. The integration of a camera module allows real-time analysis and recognition of human presence, reducing false alarms. The relay module also enables automated control of home appliances, synchronizing and feedbacking operations with sensed human presence. The dynamic adaptation of the system improves user convenience and energy efficiency.

IOT Based Distance Measuring Robot

The paper describes the development of a distance measurement system that uses ultrasonic waves and is interfaced with a microcontroller module ATmega328P and the ATMega 16U2 Processor. We know that the human audible range is 20 Hz to 20 kHz. We can use these frequency range waves with an ultrasonic sensor. The advantages of this sensor are that when it is connected to an Arduino, which is a control and sensing system, a pro per distance measurement can be done using new methodologies. As vast sums of money are spent on hundreds of inflexible circuit boards, the Arduino will enable businesses to introduce many more unique items. This distance measurement system has a wide range of industrial applications, including range meters and proximity detectors. The ultrasonic sensor's hardware is interfaced with the Arduino. This method of measurement is an effective way to accurately measure small distances. The distance between an obstruction and the sensor is measured using an ultrasonic sensor. After knowing the speed of sound, the distance can be computed. In this project, The Bluetooth sensor receives a code from the Arduino app, and the Arduino operates in accordance with the code. Additionally, an ultrasonic sensor is utilized, which transmits an ultrasonic wave towards the object. When the ultrasonic wave returns after hitting the item, the transmitter detects the obstacle's distance and displays it on the LCD in centimeters. Using the ESP32 camera module, the user may log in to a specific IP address and see the object from a remote location while also controlling the robot.

Speed Breaker and Potholes Detection Using DL and IoT

This project introduces a novel approach to enhance road safety by implementing a real-time pothole detection system. The system utilizes a camera module to capture video input, which is then processed using a Python file for Image detection. Upon detecting a pothole or a speed breaker, the system triggers a buzzer to alert nearby vehicles and pedestrians. This alert mechanism aims to reduce the risk of accidents and vehicle damage caused by potholes, thereby improving overall road safety. Additionally, the project can contribute to the efficient maintenance of roads by enabling timely repair of identified potholes. The system's effectiveness in alerting road users and facilitating prompt repairs underscores its potential to enhance the quality and safety of transportation infrastructure. Also a concept of RQI (Road Quality Index) will improve the transport and travelling over the road. And to make a database which contain coordinates where speed breakers and potholes are present and this database will be real time

An IoT-Enabled System for Prioritizing Emergency Vehicles and Detecting Accidents in Smart Traffic Management

Abstract—With the growth of urban areas and the rising number of vehicles, managing traffic congestion has become increasingly difficult, especially when it comes to ensuring prompt clearance for emergency vehicles like ambulances, fire trucks, and police cars. This paper introduces a Smart Traffic Management System powered by IoT, which leverages camera modules and machine learning algorithms to identify and prioritize emergency vehicles. Ambulances receive the highest priority, followed by fire trucks and police vehicles. The system adapts traffic signals dynamically by detecting emergency vehicles in real-time, and also enhances traffic flow using IR sensors to monitor vehicle density. Additionally, the system includes an Accident Detection Module that leverages intelligent accelerometer sensors to detect road accidents, using data on speed, pressure, and distance. This holistic approach ensures that emergency lanes are cleared efficiently and improves traffic mobility in urban environments. The proposed system significantly enhances the efficiency of traffic management compared to conventional methods, offering a comprehensive solution for reducing congestion, improving emergency response times, and minimizing accident fatalities. Index Terms—IoT, smart traffic management, machine learning, emergency vehicle detection, traffic signal optimization, IR sensors, accident detection, accelerometer sensors, real-time data processing, urban mobility, SCATS (Sydney Coordinated Adaptive Traffic System), IoT-Enabled Smart Traffic Management System (ISTMS).

Rapid acquisition and surface defects recognition based on panoramic image of small-section hydraulic tunnel

Small-section hydraulic tunnels are characterized by small spaces and various section forms, under complex environments, which makes it difficult to carry out an inspection by the mobile acquisition equipment. To resolve these problems, an arbitrarily adjustable camera module deployment method and the corresponding automatic image acquisition equipment with multi-area array cameras are proposed and developed. Such method enables the acquisition of full-length surface images of the hydraulic tunnels with different cross-section forms and diameters by a one-way travel, and the overlap rate and accuracy of the acquired image sets meet the requirements of three-dimensional reconstruction and panoramic image generation. In addition, to improve the speed and accuracy of traditional algorithms for tunnel surface defects detection, this paper proposes an improved YOLOv5s-DECA model. The algorithm introduces DenseNet to optimize the backbone feature extraction network and incorporates an efficient channel attention ECA module to make a better extraction of features of defects. The experimental results show that mAP, and F1-Score of YOLOv5-DECA are 73.4% and 74.6%, respectively, which are better than the common model in terms of accuracy and robustness. The proposed YOLOv5-DECA has great detection performance for targets with variable shapes and can solve the problem of classification imbalance in surface defects. Then, by combining YOLOv5-DECA with the direction search algorithm, a “point-ring-section” method is established to allow rapid identification of common surface defects by detecting them layer by layer with the bottom image of the stitched panorama as the seed. The presented method in this paper effectively solves the problem that a single image fails to show the overall distribution of the defects and their accurate positioning in a whole large tunnel section and the effective features of defects in an excessively large panoramic image size are difficult to be captured by the neural network. Field applications demonstrated that the presented method is adequate for high-precision and intelligent surface defect detection and positioning for different small-section hydraulic tunnels such as circular, arch-wall, and box-shaped hydraulic tunnels.

SuMOS, a submerged microscope for observing substrates: Studying benthic activity in aquatic environments

Described here is the construction of a low-cost, standalone underwater camera system designed for recording processes occurring on aquatic substrates. The Submerged Microscope for Observing Substrates (SuMOS) utilizes a Raspberry Pi Zero 2 W paired with a Raspberry Pi Camera Module v3 NoIR, with IR illumination for low-light situations. It features a waterproof housing inspired by the open-source PipeCam project, with enhancements for sealing and substrate mounting. The SuMOS system operates autonomously, capturing high-resolution images at fixed intervals under various lighting conditions. Tests in the Choptank River of Maryland demonstrated the system’s robustness in capturing patterns of amphipod activity under challenging optical conditions. This versatile tool offers a scalable solution for highly time-resolved, in situ studies of processes occurring at the interface of the aquatic/solid surface boundaries. The SuMOS provides significant advantages in cost, ease of deployment, and data collection.

Design and demonstration of DEA-based tunable planar liquid crystal Alvarez lenses

Traditional liquid crystal lenses realize the tunability of focal length by controlling the rotation angle of the liquid crystal molecule. In this paper, a tunable planar liquid crystal Alvarez lenses, which can tune the focal length via the lateral displacement of two planar liquid crystal phase plates actuated by dielectric elastomer actuators (DEA), was designed and fabricated. The maskless laser direct writing technology is employed to fabricate the tunable planar liquid crystal Alvarez lenses. The experiments show that liquid crystal Alvarez lenses achieved a wide varifocal range of 1476 mm ∼ 118 mm with lateral displacement of the two planar liquid crystal phase plates varying from 0.2 mm ∼ 2.5 mm. The proposed varifocal lens has a dynamic response with a rise time of 185 ms and a fall time of 296 ms. Experiments are designed to verify the varifocal capability of the proposed Alvarez lenses, and the experimental results show that it can focus virtual images of an object at different depths in the real environment. The proposed liquid crystal Alvarez lenses have potential applications in compact and miniaturized imaging systems such as camera modules, medical imaging, enhanced displays, etc.

A vibration transducer framework for the measurement and reconstruction of vibration signals via low-rate sampling

This study presents a novel, cost-effective, and highly accurate computer vision-based vibration transducer framework, enabling the measurement and reconstruction of vibration signals using video frames captured at a low frame per second (fps) relative to the Nyquist rate of the measured signal. In the proposed framework, an assembly consisting of several spring-mass systems serves as the primary sensor, while a camera module tracks the motion of the seismic masses as the secondary sensor. The setup, along with the modified compressive sensing equation (CS) equation, results in a transducer scheme that can recover the base motion signals from the displacement history of seismic masses sampled at rates much lower than the maximum predominant frequency of the original signal. The performance of the proposed transducer framework has been successfully verified by numerical simulations. In addition, an experimental study has been conducted in order to validate the concept under modelling and measurement error.

Realtime and noninvasive assessment of endotracheal tube displacement using near-infrared and visible cameras.

Endotracheal tube (ETT) intubation is a medical procedure routinely used for achieving mechanical ventilation in critically ill patients. Appropriate ETT placement is crucial as undetected tube migration may cause multiple complications or even fatalities. Therefore, prompt detection of unplanned movement of the ETT and immediate action to restore proper placement are essential to ensure patient safety. Despite this necessity, there is not a widely adopted tool for real-time assessment of ETT displacement. We have developed a device, a dual-camera endotracheal tube or DC-ETT, to address this unmet clinical need. This device uses a near-infrared (NIR) LED and a side-firing optical fiber embedded in the side of an ETT to light up the tracheal tissue and a visible and NIR camera module for the displacement detection. The NIR camera tracks the movement of the NIR pattern on the skin, while the visible camera is used to correct the body movements. The efficacy of the DC-ETT was assessed in two piglets with a linear displacement sensor as reference. A mean discrepancy of less than 0.5 mm between the DC-ETT and reference sensor was observed within a displacement range of ±15 mm. The results suggest that the DC-ETT can potentially provide a simple and cost-effective solution for real-time monitoring of ETT displacements in operating rooms, intensive care units, and emergency departments.

An efficient face and mask detection by embedded device via thermal imaging camera

Abstract Since 87.9% of COVID-19 patients develop a fever symptom, the mandatory wearing of masks, temperature screening, and isolation of individuals with fever are the most import methods for community epidemic prevention. This study develops a thermal imaging thermometer embedded system with face, face mask, and temperature detection capabilities using low-end CPU devices. The hardware includes a Raspberry Pi 4, FLIR Lepton 3.5 LWIR Micro Thermal Camera Module, display screen, and alarm. The system is characterized by its low cost and lightweight design, and it only needs to detect necessary areas around the face area during face mask and temperature detection, effectively avoiding external interference and further enhancing the effectiveness of epidemic prevention. The face detection model is based on the YOLO-Fastest architecture and has been improved, referred to as Pi-fast. In terms of performance on the training and testing sets, our proposed Pi-fast achieves Mean Average Precision (mAP) values of 99.64% and 93.18%, F1 score values of 0.94 and 0.87, and Intersection over Union (IoU)values of 78.07% and 65.98%, respectively. Regarding system performance, the frame rate of FLIR Lepton 3.5 Thermal Camera Module hardware specifications is 8.7 FPS. When using YOLO-Fastest and proposed Pi-fast model for detection on the proposed thermal imaging system, the frame rates are 6.58 FPS and 7.32FPS, respectively. The proposed Pi-fast model outperforms YOLO-Fastest about 0.74 FPS (11.2%). The system can detect objects at a distance of about 7 meters from the lens in thermographic camera with a resolution of only 160×120 pixels, making it suitable for multi-person screening and improving the screening process’s efficiency. The system can effectively perform temperature screening and face mask detection, making it suitable for office buildings, schools, medical clinics, and other public areas. During the severe epidemic period, the proposed thermal imaging embedded system that was actually used for epidemic prevention work at I-Shou University, improving screening efficiency and reducing labour costs.

Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management

Since the world's population is rising continuously, more cultivable land is being utilized for their dwellings. As a result, the amount of food supply is decreasing day by day. In order to address the food shortage, a proper plan and technological breakthroughs is must. Tomato is a kind of vegetable which has the healthy ingredients and essential for our daily food list. The proposed system suggests an IoT based tomato cultivation and pest management system, with the help of deep learning methods. In the IoT implementation, camera module and moisture sensor are used to collect images of tomato plant, soil condition respectively. Based on the moisture content, the water pump will supply the water when it necessary. Besides, the real-time images of tomato leaf will be sent to the server to identify and classify natural enemies like various insect species. In the proposed system seven types of pests are identified with the help of ten deep learning models like InceptionV3, Xception, InceptionResNetV2, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, DenseNet121, DenseNet169, DenseNet201. This study has trained with leaves and insects separately to identify whether an image from a tomato plant is insectoid or not. 458 images of pests and 912 images of leaves are utilized in the proposed architecture. The accuracy of classifying insects or leaves using DenseNet201 is 100 ​%. The highest accuracy of 94 ​% is obtained to classify the different insects using the DenseNet201 model.

Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing

The enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model’s classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system.

Fruit and vegetable self-billing system based on image recognition

Introduction: Shopping centers have become a necessary aspect of living, especially for city dwellers. To realize the identification and settlement of fruits and vegetables lacking bar codes is a major problem in supermarket self-service settlement. Methods: In this study, we proposed a novel Garra Rufa fish-optimized multi-objective convolutional neural network (GRFO-MCNN) for fruit and vegetable detection and freshness recognition. To improve feature identification performance, the GRFO-MCNN integrates the CBAM, which consists of the CAM and the SAM. Freshness recognition and fruit and vegetable detection are greatly enhanced by the CBAM by focusing on pertinent regions of images. Results: The proposed model integrate with the automated settlement system which transform the fruits and vegetable purchases by streamline identification and payment process. The Raspberry Pi, a microcontroller with a camera unit, makes up the suggested model to automate the billing system. For this study, we used a Raspberry Pi module to automatically acquire image data of fruits and vegetables. Conclusions: The suggested approach is contrasted with the other traditional approaches. The result shows the suggested approaches outperformed in accuracy (0.93), MAE (0.11), and RMSE (0.53). The fruits and vegetables that are arranged for automatic weighing are captured by the camera module. The microprocessor receives as an input the cost of various products per kilogram automatically. Consequently, the Raspberry Pi automatically calculates and shows the overall lprice of the products on the monitor

Benchmarking analysis of computer vision algorithms on edge devices for the real-time detection of digital dermatitis in dairy cows

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the coronary band of the foot. It causes significant animal welfare and economic losses to the cattle industry. Early detection of DD can lead to prompt treatment and decrease lameness. Current detection and staging methods require a trained individual to evaluate the interdigital space on each foot for clinical signs of DD. Computer vision (CV), a type of artificial intelligence for image analysis, has demonstrated promising results on object detection tasks. However, farms require robust solutions that can be deployed in harsh conditions including dust, debris, humidity, precipitation, other equipment issues. The study aims to train, deploy, and benchmark DD detection models on edge devices. Images were collected from commercial dairy farms with the camera facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system. Models were trained to detect and score DD lesions and embedded on an edge device. The Tiny YOLOv4 model deployed on a CV specific integrated camera module connected to a single board computer achieved a mean average precision (mAP) of 0.895, an overall prediction accuracy of 0.873, and a Cohen’s kappa of 0.830 for agreement between the computer vision model and the trained investigator. The model reached a final inference speed of 40 frames per second (FPS) and ran stably without any interruptions. The CV model was able to detect DD lesions on an edge device with high performance and speed. The CV tool can be used for early detection and prompt treatment of DD in dairy cows. Real-time detection of DD on edge device will improve health outcomes, while simultaneously decreasing labor costs. We demonstrate that the deployed model can be a low-power and portable solution for real-time detection of DD on dairy farms. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time detection of health outcomes in precision farming.

Design of a real-time image processing system based on FPGA

To enhance the real-time performance of image processing, effectively reduce video transmission bandwidth and storage space, and improve transmission efficiency, a real-time image processing system was designed using a Field Programmable Gate Array (FPGA). The system is mainly divided into image acquisition, image processing, and image display subsystems. Images are captured using a camera module and transmitted to the image processing module for processing, and finally displayed via an HDMI monitor. Measurements indicate that the system has strong real-time performance, low power consumption, and high portability, making it valuable in fields such as surveillance and security, medical imaging, and industrial automation.

Ultrasonic aperture-tunable gel lens

Conventional camera modules require mechanical moving parts to move their lenses and to adjust their focal points. This paper examines optically tunable lenses with a focal length and lens aperture that can be controlled using ultrasound vibration and a transparent gel. The lens uses the acoustic radiation force, which induces changes in the lens profile; varifocal convex and concave lenses can be fabricated by adjusting the input signals. The optical characteristics of the lenses were evaluated using ray tracing simulations. The aperture can be controlled with the driving frequency, with higher frequencies leading to a wider range of focal length changes with a lower input voltage.

DESIGNING OF COMPUTER VISION BASED SAFETY GEAR DETECTION USING YOLO V8 AND JETSON NANO

Abstract—Enhancing worker safety on construction sites is pivotal, as lapses in wearing safety gear such as helmets, masks, and vests can lead to potential hazards. To address this issue, we introduce a novel solution leveraging the YOLO V8 device to detect helmet usage among workers. However, challenges arise due to security unawareness and discomfort, causing helmet removal. To mitigate this risk, we propose an advanced system employing the Jetson Nano board and . This system not only detects helmet adherence but also monitors the use of safety masks and vests. Unlike the existing YOLO V5-based approach, our proposed system adopts YOLO V8, ensuring heightened accuracy. The integration of a CSI Camera Module further enhances real-time monitoring capabilities. Through the synergy of Jetson Nano, YOLO V8 and CSI Camera Module, we pioneer an innovative solution to enforce safety compliance by preventing unauthorized entry without proper safety measures. Index Terms— NVIDIA Jetson Nano, Object detection, Deep Learning, YOLO V8, CSI Camera module.

CNN and Adaboost fusion model for multiface recognition based automated verification system of students attendance

In recent times, companies and institutions globally are increasingly adopting automated systems for recording employee attendance due to the inefficiency and error-prone nature of traditional methods. Face recognition is the fastest, most natural, and most accurate way to identify someone, despite its difficulty. Remote deployment and control of the technology using internet of things (IoT) protocols provides real-time attendance data worldwide. We use the Haar-cascade algorithm to detect and extract features and the adaptive boost algorithm confused with convolutional neural network (CNN) algorithm to recognize the face in our proposed smart attendance system. Per frame, the proposed system recognizes multiple faces. Face recognition in 18 conditions was designed into the proposed system to ensure its versatility. The system's graphical user interface (GUI) was made for average users. This work is more important because IoT technology records student attendance and sends data to authorities. We use Raspberry Pi 4 and camera module for our suggested system. Python and OpenCV libraries tested the multiple face image recognition proposal in 18 situations under four conditions. Single-face image recognition was compared to other methods. In most cases, the proposed method was 100% accurate and outperformed related methods.

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.

Real-Time Remote-Controlled Human Manipulation Medical Robot Using IoT Module

INTRODUCTION: Innovative robotics and advanced computer vision technology converge in the Human Manipulation-Controlled Robot, utilized for medical applications. The robot operates through human gestures, and includes a camera module for real-time visual feedback, enhancing its functionality and user interaction. OBJECTIVES: The primary goal of the research was to harness the natural expressiveness of human gestures to provide a more intuitive and engaging method of controlling medical robots. The focus is on enabling precise control through programmed responses to specific gestures, ensuring effective interaction with medical tasks. METHODS: The robot’s hardware configuration consists of a mobile platform with motorized components, an ESP32 module, gesture recognition sensors and a camera modules. The ESP32 module interprets signals from gesture recognition sensors to execute precise commands for the robot's movements and actions. Simultaneously, the camera module captures live footage, providing visual feedback through an intuitive interface for seamless interaction. RESULTS: The Human Manipulation-Controlled Robot has been successfully developed, featuring a fetch arm capable of autonomous movement and object manipulation. This research address critical needs in medical centers, demonstrating the feasibility of using only minimalistic EEG electrode wireless transmission to operate a robot effectively. CONCLUSION: Through the provision of a more intuitive and engaging method of controlling and interacting with medical robots, this innovation has the potential to significantly improve user experience. It represents a most important development in medical robotic vehicles, enhancing user experience and operational efficiency through advanced human-robot interaction techniques.