Webcam Camera Research Articles

DETEKSI SUHU TUBUH DAN MASKER WAJAH DENGAN MLX90614, OPENCV, KERAS/TENSORFLOW, DAN DEEP LEARNING

<p><em>Digital image processing technology combined with sensors is currently being used. This technology can help various needs such as education, industry and health. During the COVID-19 crisis, people are required to wear masks for protection. The public is also required to check their temperature regularly, which will have a significant health impact. This can reduce the risk of transmitting the Covid-19 virus. In this study, the author uses a WebCam camera and a temperature sensor MLX90614 as a tool to monitor the use of masks and measure body temperature. The author uses OpenCV for digital image processing and Tensorflow as a deep learning method for mask detection. The result of this study is that Tensorflow can detect wearing a mask with 99% accuracy. The MLX90614 sensor can measure body temperature with 99% accuracy at a reading distance of 5 cm to 10 cm.</em></p>

RANCANG BANGUN PROTOTYPE SISTEM PENDETEKSI PELANGGARAN PADA ZEBRA CROSS DI LAMPU LALU LINTAS BERBASIS ARDUINO

Current road users cannot be separated from the number of violators, therefore traffic lights are made to regulate traffic on the road. At traffic lights, there is also a zebra crossing which serves as a means of crossing the road for pedestrians. To minimize violations at road intersections, researchers designed a tool to detect traffic violations.
 Traffic violation detection tool is made in prototype form using a control system with Arduino nano and software. This traffic light system uses LDR and laser sensors to detect these violations by cutting the laser which sends light to the LDR. This tool is also equipped with a webcam camera that functions to photograph violations that occur and a buzzer that functions as a warning to officers and riders in the event of a violation with an average response speed of the webcam of 2.37 seconds and the average response speed of the buzzer is 0.4 seconds. . The snapshot from the webcam is saved automatically on your PC / Laptop.

Identification and Classification of Pathogenic Bacteria Using the K-Nearest Neighbor Method

Bacteria are a group of living things or organisms that do not have a core covering. In the grouping, some bacteria are pathogenic. With a microscopic size, many pathogenic bacteria are found around and spread through the food eaten or by touching objects around them, then cause diseases such as diarrhea, vomiting, and others. As a more effective effort to help the government and society prevent disease caused by pathogenic bacteria, a system for the identification and classification of pathogenic bacteria K-Nearest Neighbor was created. This system uses a biological microscope that is attached to a webcam camera above the ocular lens as a tool to see bacterial objects and assist in bacterial capture. Rough player rotates automatically (auto-focus) in image capture. In the process of classification and identifying bacteria, the K-Nearest Neighbor method is used, which is a method with the calculation of the nearest neighbor or calculation based on the level of similarity to the dataset. In this study, the bacteria vibrio chlorae, staphylococcus aereus, and streptococcus m. with the highest accuracy is the K = 9 value of 97.77% using the Chebyshev method.

Implementasi Algoritma Haar Cascade pada Aplikasi Pengenalan Wajah

The large number of citizens in an organization makes the development of an attendance system or citizen detection in a place important in the running of work activities in the organization. Utilization of an IP Camera which is only used for regular monitoring without further detection of the needs of citizens in the organization made the development of personnel detection developed for monitoring the presence of personnel. With the development of a face detection system, it is hoped that the facial algorithm development system will be developed using an IP Camera. Face detection has been developed which has many and special features which aim to determine whether or not a face has been detected in an image. With image management that is developed in face detection, detection will be faster and more accurate because the color is processed into gray degrees so that there are fewer color pixels than those with colors. By using the Python programming language and an image detection library called OpenCV, less code will be designed. This study uses the Viola Jones method, which is a fast and accurate face detection method developed by Paul Viola and Michael Jones. In this study, the Viola Jones method uses the Haar Cascade algorithm which functions as a detection feature in the system and is combined with the internal image process and the AdaBoost Learning and Cascade Classifier so that the detected face object will easily classify whether the object is a face or not. In this case the Cascade Classfier used in this study is the face and eyes. The development of this algorithm is carried out for face detection and recognition. The detection is done by taking pictures with the process taken using a webcam. The system will take several pictures and then the image data will be stored in a folder called dataSet. After that, all data is trained so that it can be recognized by the system. With retrieval, detection and recognition limitations that can only be taken from a distance of less than three meters, face detection on the IP Camera can still read objects other than faces. With recognition and accuracy on the webcam camera, about 80,5% this system can be developed with the Haar Cascade algorithm and the Haar Cascade algorithm precisely to be applied to the development of faced detection and face recognition. By developing the Haar Cascade algorithm for face detection, problems and utilization of an organization's data can be more easily detected and used by IP cameras that can support the performance process of face detection and recognition

PENDETEKSIAN PENGGUNAAN MASKER WAJAH DENGAN METODE CONVOLUTIONAL NEURAL NETWORK

“Face Mask Detection Using the Convolutional Neural Network” is a PC based program that aims to detect and classify human beings whether a person is using a mask or not with access through a webcam camera. This program is created using the Python language with several libraries. The classification of face masks uses the Convolutional Neural Network method with the MobileNetV2 architecture. Meanwhile, human face detection uses the Haarcascade Classifier. How the program works is by accessing the connected camera and if the person detected is wearing a mask, the person will be labeled "using a mask" and given a green box to mark the detection along with the analysis value, whereas if not, it will be labeled "not using a mask" and a red box with also the predicted value. From the test results, it can be proven that the accuracy program is good enough to detect the use of face masks with an average object detection accuracy of 88.53% and the classifier for the use of mask an average of 84.45%.

MIPI Camera: Opportunities, Challenges and Solutions for Chromebook Cameras

Smartphone cameras revolutionized for at least two generations in the past decade; i.e. megapixel enthusiasm and multi-camera combination. However, most laptops are still with low resolution fixed focused webcam cameras. The story could have changed recently. The COVID-19 pandemic keeps people working from home; therefore, video conferencing becomes part of the new normal of daily life. The camera quality of laptop computers is in the spotlight when users join video conferences using their laptops webcam. We are working on a MIPI camera solution to drive the Chromebook webcam quality with minimum impact of cost. There are several challenges by porting the current smartphone MIPI camera technology to Chromebook directly: miniature module size and challenge of the hardware product design, limited ISP. There is also no complete evaluation criterion to video conferencing quality. We will discuss each aspect one by one.

Temperature Monitoring System for Egg Incubators Using Raspberry Pi3 Based on Internet of Things (IoT)

The incubator is made as a substitute for hatching naturally at the same time. The success of the hatch machine is largely determined by the temperature stability in the incubator. In the use of small-scale hatcheries for native chicken hatching, they are still faced with the problem of low hatchability due to one of the obstacles, namely the power outage during the hatching process. To improve the monitoring performance of egg incubators, the writer wishes to conduct research "Monitoring to Control and Monitor Temperature in Egg Incubators" using a webcam camera to monitor temperature conditions and hatch eggs. The working system is the DHT11 sensor will detect the temperature, the webcam camera in real time will monitor the state of the eggs then the raspberry pi3 will automatically control the temperature and electrical energy on the incubator, the smartphone monitors and can also control the temperature with the state of the eggs in realtime, 12V battery as a replacement energy when the PLN goes out. From the results of the tests carried out, the authors can draw conclusions, namely: The webcam camera can display the condition of the eggs in the incubator room. Android smartphones can receive information with a webcam camera during the hatching process. The data logger can display recapitulated temperature and humidity data.

Deteksi Objek Menggunakan Metode Single Shot Multibox Detector Pada Alat Bantu Tongkat Tunanetra Berbasis Kamera

Blind people have difficulty in navigating due to the limited sensing they are capable of. In this research, we design a stick tool that can distinguish objects in the form of humans, animals and inanimate object based on camera. Processing is carried out with the Raspberry Pi with a webcam camera as input and indicators in the form of a buzzer and vibrator. The feature extraction process is carried out by deep learning using the tensorflow library and image processing using the Single Shot MultiBox Detector (SSD) method. Tests were carried out on human objects, animals (cats), and inanimate objects (chairs and tables) for indoor and outdoor conditions and obtained an accuracy of 92%, a sensitivity of 83%, and a specificity of 100%.

RANCANG BANGUN PERTANIAN STROBERI BERBASIS INTERNET OF THINGS

Technological developments have been utilized in various fields of science, includingagriculture. Technology support in agriculture has been proven to be able to provide betterproduction results. In this study, an IoT-based agricultural environmental monitoring systemwas developed. The system was built using Raspberry Pi as a microcontroller and dataprocessing center, equipped with a moisture sensor and a DHT 11 temperature sensor. Thissystem was applied to a laboratory-scale strawberry farm. The system works automatically toregulate the temperature and humidity of the plant environment. If the temperature andhumidity of the strawberry plants do not meet the standards, the pump will automatically waterto keep the strawberry plants at the appropriate temperature and humidity. The system is alsoequipped with a notification feature so that strawberry farmers can obtain information throughtelegram media when the temperature of the strawberry plants is hot and the soil of thestrawberry plants is dry. This tool is also equipped with a webcam camera as an additionalfeature that serves to detect the level of ripeness of the strawberries in terms of the color of thefruit. The camera will detect the color of the strawberry, and if the strawberry is red, thestrawberry is categorized as ripe, and the farmer will get a notification via telegram in the formof a picture that the strawberry is ready to be harvested.

Perception, Planning and Control for Self-Driving System Based on On-board Sensors

The autonomous vehicle can recognize and understand environment, self-control, and achieve the driving level of the human driver. To create this kind of system, the following work was carried out: (a) a real time lane detection system is proposed, based on vision system functions, using webcam camera; (b) In order to detect curve lanes, deep learning is applied for lane detection, based on fully Convolutional Neural Network (CNN); (c) The cubic spline interpolation method is used for path generation, based on Global Positioning System (GPS) data, where distance between two adjacent path generation points is same. Compared with the connection method of the cubic polynomial fitting algorithm, the curve fitted path by the cubic spline interpolation method is smoother and more satisfied with the vehicle motion pattern; (d) Based on frenet coordinate frame, optimize trajectory planning method is used to plan safe, easy and comfort trajectory. Compared to the Cartesian coordinate frame, frenet coordinate frame simplifies the solution of road curve fitting problems, especially in the case of complex road environment; (e) Fuzzy sliding mode control method is proposed for vehicle steering control. Finally, the simulation and experiment results are used to prove the effectiveness of the proposed methods.

Simple Cost-Effective Sequential Injection Lab at Valve with Remote Control Employing Everyday Communication Technology with a Webcam Camera Detector for the Determination of Iron and Phosphate as Model Analytes

Simple Cost-Effective Sequential Injection Lab at Valve with Remote Control Employing Everyday Communication Technology with a Webcam Camera Detector for the Determination of Iron and Phosphate as Model Analytes

Developing a Smart Nursery Application for Monitoring and Babies Care

This paper presented a Smart Nursery application for busy parents so that they can ensure the safety and proper care for their babies. This system can show the baby’s motion, position and sound through live web-cam camera which is provided by the proposed smart application so that the parents or another responsible person can monitor the baby while away from him or her. This proposed smart application can provide a convenient and easy way for busy parents to take care of their babies. The proposed system was analyzed, design and developed using the Unified Modeling Language (UML), ASP.NET and HTML.

Detection of Object Changes in Low Contrast Image Using Template Matching Method

This research uses a contrast stretching method to improve the detected object image. This process is needed because of the lack of lighting in the room so it is difficult to identify properly. This research develops an image object change detection system to detect the movement of objects contained in a room through a Closed Circuit Television (CCTV) camera which is conducted simulations using a webcam camera using a template matching method with normalized cross-correlation techniques. The results of detecting changes in image objects are then made to compare between the reference image and image before there is an object change with the image after the object changes. System testing is carried out at a certain time, namely 07.00-17.00 WIB. The level of accuracy to identify the detection of changes in image objects using the template matching method with normalized cross-correlation technique is 85% for the category of no change in min (0.01025) and max (0.02088) and changes in min (0.01640) and max (-0.00513).

N3-AUV (Nusantara 3-Autonomous Underwater Vehicle): design and implementation for underwater exploration

Autonomous Underwater Vehicle (AUV) is a type of underwater robot that can move and perform pre-assigned the missions based on a program which is installed in the microprocessor in the body of the vehicle. AUV uses thrusters to maneuver underwater and may be supported by a compass, various sensors, and a camera that are attached to the vehicle. In this paper, we present the development of N3-AUV and a pool test of this performance. N3-AUV uses 8 thrusters, 2 webcam cameras, Raspberry Pi, a depth sensor, and a compass. The vehicle has a torpedo-like shape so that it is easier to move underwater relatively faster. With 2 webcam cameras, where the first one is placed under the frame to record images of the conditions beneath the vehicle and the second one is placed in front of the frame to record the conditions in front of the vehicle. A pool test was conducted by means of pre-assigning missions, such as navigating pass the goal post, and finding and knocking down an underwater object. N3-AUV can complete three mission of four. Missions that have been carried out successfully are navigation, acquisition, and localization. N3-AUV has a maximum speed of about 1 m/s.

Design of An Automatic Follower Shopping Trolley Based on Image Processing

In the era of industrial revolution 4.0, all human activities are replaced by machines. Humans are required to optimize brain function than muscle function. All human work is facilitated by robots or tools work automatically. For example, shopping activities in the modern market. Consumers prefer to shop in the modern market than the traditional market, because the items are complete, neatly arranged, and they have facilities like trolleys as transporters. Trolleys can be pushed and pulled easily. But when shopping, parents have a problem with pushing or pulling a trolley when they carrying their children. Therefore, we created a design for automatic follower shopping trolley based on image processing which can follow the consumers when shopping. This trolley has a webcam camera as a sensor to take pictures of the objects or special accessories that used by consumers. The result of data are processed in such a way and then it will be obtained the x and y coordinates positions of the consumer and the next data will be sent to the Raspberry Pi 3 which will automatically determine the movement of the trolley. The test results showed that the distance range detected between trolleys and consumers ranged from 0.5 meters to 2 meters, and trolleys can move according to the point where the object was detected. Thus, the trolley can follow the user well.

Body Height Detection System Using Russel & Rao Method

Height body detection is an interesting thing to study, because of height is often using in various fields. Measurement height with a manual tool, it is very easy to use. Retrieval of objects using a webcam camera then measured with the Russel & Rao method. This system was built using Borland Delphi 07 programming language. His research method was obtained from literature studies of various books and articles which consisted of several stages, like the grayscale stage, and russel & rao. And at the time of height detection, this system will doing well if around the object that will be taken into account there are no other objects, such as the meter will move to another thing. So to simplify the measurement, you must choose the right place. Russell & Rao’s performance has a true detection percentage of 65%. Based on 8 sample tests that have been carried out, only 3 have been detected correctly.

Security System Implementation of Safe Deposit Box Using Iris Pattern Based Matlab

This research explains about a biometric system that uses iris pattern for Safe Deposit Box Users. Matlab is used for processing the iris pattern. In the process of image processing, there are several processes such as segmentation, extraction and identification. The initial phase of segmentation will be proceed using canny edge detection then continued by hough circle transform to detect the center circle of iris. The second process is by extracting where the image of segmentation will be proceed by using haar wavelet transformation, the image will be changed to another form by decomposition of the vertical and horizontal image of the iris in order to get the data from iris image of user. The last phase is identification. In this stage, hamming distance method that will read the iris data in the database that has get through both phases and compare it with image captured by the webcam camera. If the image of user has been detected, then the safe deposit box will be opened. The advantages of this system is the iris pattern is very stable and different form each individual so it is not easy to be manipulated. The average identifying of this research is 76.70%. As security system, using iris pattern can be developed to prevent unwanted act.

Digital Image Processing for Detecting Industrial Machine Work Failure with Quantization Vector Learning Method

Todays, digital image processing is widely used in various fields to facilitate humans in doing work by analyzing videos or images for use in decision making in the industrial world. The use of industrial machine technology is one of the most important factors in efforts to facilitate human work, but an industrial machine is inseparable from work failure that can hinder the production process and cause harm to the industry. This study aims to detect a failure in industrial machinery by using video data of industrial machine movements recorded using a webcam camera. For the preprocessing stage, the image is resized then converted to grayscale imagery and segmented using the thresholding method, then morphological operations are performed with an opening operation, feature extraction is done by changing the binary image into a vector data that is used as input data in the classification process using the Learning Vector Quantization Neural Network Algorithm (LVQ NN) version 1. The results showed the results of the detection of machine working errors can be done well with an accuracy value of 94.24% in training and 92.38% in the testing phase.

Vehicle Collision Detection Application Through Collision Video Files with Quadtree Algorithms

The improvement of digital image processing technology is increasingly rapid. Image processing has been widely used to maximize the usefulness of the webcam interface or CCTV, which can be used to monitor traffic flow on the street. One of the frequent event occure on the street is a crash (collision). This problem often arises in a collision event is a dispute about the party guilty in the collision. A collision detection application using Quadtree algorithm can solve this problem. This study aims to analyze and design intelligent systems for vehicle collision detection on street through a webcam camera. Using the Quadtree algorithm, a collisions that occur on vehicles can be detected by the regions division method such ash Quadtree algorithm. Using this application the collision possibilities can be describe. Based on the results of the tests conducted, information was obtained that the accuracy of the application of the Quadtree algorithm in the detection of car collisions on the highway was 8: 2 where the value was obtained from the number of collisions detected.

PERANCANGAN SISTEM DETEKSI OBJEK PADA ROBOT KRSBI BERBASIS MINI PC RASPBERRY PI 3

KRSBI Wheeled is One of the competitions on the Indonesian Robot Contest,. It is a football match that plays 3 robot full autonomous versus other teams. The robot uses a drive in the form of wheels that are controlled in such a way, to be able to do the work the robot uses a camera sensor mounted on the front of the robot, while for movement in the paper author uses 3 omni wheel so the robot can move in all directions to make it easier towards the ball object. For the purposes of image processing and input and output processing the author uses a Single Board Computer Raspberry PI 3 are programmed using the Python programming language with OpenCV image processing library, to optimize the work of Single Board Computer(SBC) Raspberry PI 3 Mini PC assisted by the Microcontroller Arduino Mega 2560. Both devices are connected serially via the USB port. Raspberry PI will process the image data obtained webcam camera input. Next, If the ball object can be detected the object's position coordinates will be encoded in character and sent to the Microcontroller Arduino Mega 2560. Furthermore, Arduino mega 2560 will process data to drive the motors so that can move towards the position of the ball object. Based on the data from the maximum distance test results that can be read by the camera sensor to be able to detect a ball object is �5 meters with a maximum viewing angle of 120 �.