scholarly journals RANCANG BANGUN PENGEMBANGAN PINTU OTOMATIS PENDETEKSI MASKER DAN SUHU TUBUH MENGGUNAKAN RASPBERRY PI 4

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Teuku Radhi Muhammad Fitrah ◽  
Yudha Nurdin ◽  
Roslidar Roslidar
Keyword(s):  
Deep Learning ◽  
Raspberry Pi

Pada masa pandemi COVID-19 saat ini, pemerintah memberlakukan peraturan yaitu ketika akan masuk ke dalam ruangan (khususnya gedung publik) diharuskan mematuhi protokol kesehatan berupa menggunakan masker dan dilakukan pengukuran suhu tubuh. Namun banyak dari masyarakat yang tidak mematuhi peraturan tersebut sehingga apabila memasuki suatu ruangan yang berisi banyak orang dan tanpa  protokol kesehatan akan berpotensi terpapar virus COVID-19. Salah satu solusi untuk mengimplementasikan protokol kesehatan tersebut adalah dengan menggunakan pintu otomatis yang dapat terbuka dengan sendirinya apabila seseorang memakai masker dan suhu tubuhnya kurang dari 38 ̊ C. Pada penelitian ini akan dibuat sebuah prototipe pintu yang mendeteksi penggunaan masker dan suhu tubuh dengan kamera dan sensor suhu tubuh. Penelitian ini menggunakan metode deep learning untuk mendeteksi masker dan pengukuran sensor suhu tubuh untuk mendeteksi suhu tubuh serta sebagai pemrosesan sensor, aktuator dan komponen lainnya digunakan raspberry pi 4. Hasil dari penelitian ini berupa prototipe pintu otomatis yang akan bekerja saat user berada pada posisi ≤ 6 cm, Adapun kondisi yang harus terpenuhi agar pintu terbuka adalah user memakai masker dan suhu tubuh 38 ̊ C maka pintu terbuka, user memakai masker dan suhu tubuh ≥ 38 ̊ C maka buzzer berbunyi dan pintu tidak terbuka, user tidak memakai masker dan suhu tubuh 38 ̊ C maka buzzer berbunyi dan pintu tidak terbuka, user tidak memakai masker dan suhu tubuh ≥ 38 ̊ C maka buzzer berbunyi dan pintu tidak akan terbuka. Adapun hasil akurasi deteksi masker tertinggi yaitu pada masker kn95 dengan akurasi 99.95 % dan pendeteksian suhu akurat pada jarak 2 cm yang menghasilkan galat 0.05%. Dengan demikian prototipe pintu otomatis telah diuji dan berjalan dengan baik mengikuti kondisi yang ditentukan.

scholarly journals Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi

2021 ◽  
Vol 5 (1) ◽  
pp. 107-113
Author(s):  
Kahlil Muchtar ◽  
Chairuman ◽  
Yudha Nurdin ◽  
Afdhal Afdhal
Keyword(s):  
Deep Learning ◽  
Leaf Spot ◽  
Raspberry Pi ◽  
Leaf Spot Disease ◽  
Detection Accuracy ◽  
Crop Failure ◽  
Tomato Production ◽  
Spot Disease ◽  
Average Accuracy ◽  
Septoria Leaf Spot

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.  

scholarly journals IoT programming to Develop Self Driving Robotics Car using OpenCV and Other Emerging Technologies

2020 ◽  
Author(s):  
Mohammed Maaz ◽  
Sabah Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Self ◽  
Reverse Direction ◽  
Raspberry Pi ◽  
Specific Direction ◽  
Different Types ◽  
Practical Model ◽  
Individual Effort

<p>The advancement of Artificial Intelligence & Deep Learning has catalyzed the field of technology. The progression in these fields is exponentially increasing, and the discoveries which were once just an imagination are now changed into reality. The evolution of cars each year has made a lot of difference in people travelling from one place to another. One such reform involving Artificial Intelligence & Deep Learning is the birth of a self-driving car. The future is here where one can reach their destination hassle-free safely without the fear of accidents. This paper introduces a practical model of the self-driving robotics car, which can travel from one position to another on different types of tracks. A Pi-camera module is attached with the help of Raspberry Pi, which sends series of image frames to the Convolutional neural network, which then foretells the car to move in a specific direction, i.e. right, left, forward and reverse direction. The outcome is the robotics car, which travels in the desired direction without any individual effort.<br></p>

scholarly journals Deep Learning-Based Embedded System for Carabao Mango (Mangifera Indica L.) Sorting

2019 ◽  
Vol 8 (2) ◽  
pp. 5456-5462
Keyword(s):  
Deep Learning ◽  
Embedded System ◽  
Mangifera Indica ◽  
Raspberry Pi ◽  
Single Shot ◽  
Embedded Computer ◽  
Quality Classification ◽  
Learning Techniques ◽  
Multiple Classification ◽  
Basic Quality

This paper presents the design and development of an embedded system for ‘Carabao’ or Philippine mango sorting utilizing deep learning techniques. In particular, the proposed system initially takes as input a top view image of the mango, which is consequently rolled over to evaluate every sides. The input images were processed by Single Shot MultiBox Detector (SSD) MobileNet for mango detection and Multi-Task Learning Convolutional Neural Network (MTL-CNN) for classification/sorting ripeness and basic quality, running on an embedded computer, i.e. Raspberry Pi 3. Our dataset consisting of 2800 mango images derived from about 270 distinct mango fruits were annotated for multiple classification tasks, namely, basic quality (defective or good) and ripeness (green, semi-ripe, and ripe). The mango detection results achieved a total precision score of 0.92 and a mean average precision (mAP) of over 0.8 in the final checkpoint. The basic quality classification accuracy results were 0.98 and 0.92, respectively, for defective and good quality, while the ripeness classification for green, ripe, and semi-ripe were 1.0, 1.0, and 0.91, respectively. Overall, the results demonstrated the feasibility of our proposed embedded system for image-based Carabao mango sorting using deep learning techniques.

scholarly journals Deep Learning based Arrhythmia Classification with an ECG Acquisition System

2019 ◽  
Vol 9 (2) ◽  
pp. 3849-3852
Keyword(s):  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Data Set ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Peak Detection Algorithm ◽  
Different Types ◽  
Testing Accuracy

One of the issues that the human body faces is arrhythmia, a condition where the human heartbeat is either irregular, too slow or too fast. One of the ways to diagnose arrhythmia is by using ECG signals, the best diagnostic tool for detection of arrhythmia. This paper describes a deep learning approach to check whether signs of arrhythmia, in a given input signal, are present or not. A batch normalized CNN is used to classify the ECG signals based on the different types of arrhythmia. The model has achieved 96.39% training accuracy and 97% testing accuracy. The ECG signals are classified into five classes namely: Normal beats, Premature Ventricular Contraction (PVC) beats, Right Bundle Branch Block (RBBB) beats, Left Bundle Branch Block (LBBB) beats and Paced beats. A peak detection algorithm with six simple steps is designed to detect R-peaks from the ECG signals. A hardware device is built using Raspberry Pi to acquire ECG signals, which are then sent to the trained CNN for classification. The data-set for training is obtained from the MIT-BIH repository. Keras and Tensorflow libraries are used to design and develop the CNN and an application is designed using ’MEAN’ stack and ’Flask’ based servers.

scholarly journals Smart Attendance Management System Using Raspberry Pi and Deep Learning Technique

2021 ◽  
Author(s):  
Ashok Kumar L ◽  
Indragandhi V ◽  
Chitra A ◽  
Rajat PauL ◽  
Saswata Banerjee
Keyword(s):  
Deep Learning ◽  
Management System ◽  
Raspberry Pi ◽  
Learning Technique

scholarly journals Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sun-Kuk Noh
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Object Recognition ◽  
Environmental Pollution ◽  
Classification System ◽  
Raw Material ◽  
Raspberry Pi ◽  
Utilization Rate ◽  
Automation System ◽  
Labor Costs

Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short-term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers’ health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer-learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know-how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.

Low Cost Gunshot Detection using Deep Learning on the Raspberry Pi

2019 ◽  
Author(s):  
Alex Morehead ◽  
Lauren Ogden ◽  
Gabe Magee ◽  
Ryan Hosler ◽  
Bruce White ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Raspberry Pi

Weapon and Object Detection Using Mobile-Net SSD Model in Deep Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 1573-1582
Author(s):  
S Gopi Naik
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Optimization Algorithms ◽  
Object Identification ◽  
Integrated System ◽  
Raspberry Pi ◽  
High Level

Abstract: The plan is to establish an integrated system that can manage high-quality visual information and also detect weapons quickly and efficiently. It is obtained by integrating ARM-based computer vision and optimization algorithms with deep neural networks able to detect the presence of a threat. The whole system is connected to a Raspberry Pi module, which will capture live broadcasting and evaluate it using a deep convolutional neural network. Due to the intimate interaction between object identification and video and image analysis in real-time objects, By generating sophisticated ensembles that incorporate various low-level picture features with high-level information from object detection and scenario classifiers, their performance can quickly plateau. Deep learning models, which can learn semantic, high-level, deeper features, have been developed to overcome the issues that are present in optimization algorithms. It presents a review of deep learning based object detection frameworks that use Convolutional Neural Network layers for better understanding of object detection. The Mobile-Net SSD model behaves differently in network design, training methods, and optimization functions, among other things. The crime rate in suspicious areas has been reduced as a consequence of weapon detection. However, security is always a major concern in human life. The Raspberry Pi module, or computer vision, has been extensively used in the detection and monitoring of weapons. Due to the growing rate of human safety protection, privacy and the integration of live broadcasting systems which can detect and analyse images, suspicious areas are becoming indispensable in intelligence. This process uses a Mobile-Net SSD algorithm to achieve automatic weapons and object detection. Keywords: Computer Vision, Weapon and Object Detection, Raspberry Pi Camera, RTSP, SMTP, Mobile-Net SSD, CNN, Artificial Intelligence.

scholarly journals Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1988 ◽  
Author(s):  
Ariana Moncada ◽  
Walter Richardson ◽  
Rolando Vega-Avila
Keyword(s):  
Deep Learning ◽  
Solar Irradiance ◽  
Imaging System ◽  
San Antonio ◽  
Raspberry Pi ◽  
Solar Panels ◽  
Original Design ◽  
Error Metrics ◽  
Ill Posed ◽  
Pv Power Generation

Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera. Reconfigurable for different operational environments, it has been deployed at the National Renewable Energy Laboratory (NREL), Joint Base San Antonio, and two locations in the Canary Islands. The original design used optical flow to extrapolate cloud positions, followed by ray-tracing to predict shadow locations on solar panels. The latter problem is mathematically ill-posed. This paper details an alternative strategy that uses artificial intelligence (AI) to forecast irradiance directly from an extracted subimage surrounding the sun. Several different AI models are compared including Deep Learning and Gradient Boosted Trees. Results and error metrics are presented for a total of 147 days of NREL data collected during the period from October 2015 to May 2016.

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