scholarly journals Real-time human detection for electricity conservation using pruned-SSD and arduino

2021 ◽  
Vol 11 (2) ◽  
pp. 1510
Author(s):  
Ushasukhanya S. ◽  
Jothilakshmi S.
Keyword(s):  
Real Time ◽  
Power Supply ◽  
Computational Cost ◽  
Human Detection ◽  
Single Shot ◽  
Electric Power Supply ◽  
Tuning Method ◽  
Shot Detection ◽  
Average Accuracy ◽  
Electricity Conservation

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.

Detection of Hands for Hand-Controlled Skyfall Game in Real Time Using CNN

2020 ◽  
Vol 10 (2) ◽  
pp. 15-25
Author(s):  
Neha B. ◽  
Naveen V. ◽  
Angelin Gladston
Keyword(s):  
Real Time ◽  
Detection Methods ◽  
Input Device ◽  
Single Shot ◽  
Hand Detection ◽  
Web Based ◽  
Fast Detection ◽  
Detection And Tracking ◽  
Average Accuracy ◽  
Direct Use

With human-computer interaction technology evolving, direct use of the hand as an input device is of wide attraction. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection. This paper focuses on creating a hand-controlled web-based skyfall game by building a real time hand detection using CNN-based technique. A CNN network, which uses a MobileNet as the feature extractor along with the single shot detector framework, is used to achieve a robust and fast detection of hand location and tracking. Along with detection and tracking of hand, skyfall game has been designed to play using hand in real time with tensor flow framework. This way of designing the game where hand is used as input to control the paddle of skyfall game improved the player interaction and interest towards playing the game. This model of CNN network used egohands dataset for detecting and tracking the hands in real time and produced an average accuracy of 0.9 for open hands and 0.6 for closed hands which in turn improved player and game interactions.

scholarly journals Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks

2021 ◽  
Vol 56 (2) ◽  
pp. 235-248
Author(s):  
Fatchul Arifin ◽  
Herjuna Artanto ◽  
Nurhasanah ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Network Models ◽  
Single Shot ◽  
Neural Network Models ◽  
X Ray ◽  
Detection Model ◽  
Shot Detection ◽  
Average Accuracy ◽  
Chest X Ray

COVID-19 is a new disease with a very rapid and tremendous spread. The most important thing needed now is a COVID-19 early detection system that is fast, easy to use, portable, and affordable. Various studies on desktop-based detection using Convolutional Neural Networks have been successfully conducted. However, no research has yet applied mobile-based detection, which requires low computational cost. Therefore, this research aims to produce a COVID-19 early detection system based on chest X-ray images using Convolutional Neural Network models to be deployed in mobile applications. It is expected that the proposed Convolutional Neural Network models can detect COVID-19 quickly, economically, and accurately. The used architecture is MobileNet's Single Shot Detection. The advantage of the Single Shot Detection MobileNet models is that they are lightweight to be applied to mobile-based devices. Therefore, these two versions will also be tested, which one is better. Both models have successfully detected COVID-19, normal, and viral pneumonia conditions with an average overall accuracy of 93.24% based on the test results. The Single Shot Detection MobileNet V1 model can detect COVID-19 with an average accuracy of 83.7%, while the Single Shot Detection MobileNet V2 Single Shot Detection model can detect COVID-19 with an average accuracy of 87.5%. Based on the research conducted, it can be concluded that the approach to detecting chest X-rays of COVID-19 can be detected using the MobileNet Single Shot Detection model. Besides, the V2 model shows better performance than the V1. Therefore, this model can be applied to increase the speed and affordability of COVID-19 detection.

Single shot detection for detecting real-time flying objects for unmanned aerial vehicle

2021 ◽  
pp. 37-53
Author(s):  
Sampurna Mandal ◽  
Sk Md Basharat Mones ◽  
Arshavee Das ◽  
Valentina E. Balas ◽  
Rabindra Nath Shaw ◽  
...  
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Single Shot ◽  
Shot Detection ◽  
Aerial Vehicle

scholarly journals SMART ELECTRICITY CONSERVATION SYSTEM USING EFFICIENTNET

2021 ◽  
Vol 9 (2) ◽  
pp. 978-983
Author(s):  
Ushasukhanya S, Et. al.
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Electric Power ◽  
Power Supply ◽  
Electric Power Supply ◽  
Novel Technique ◽  
Learning Technique ◽  
Energy Conserving ◽  
Arduino Microcontroller ◽  
Electricity Conservation

Conservation of electric resource has been one of the important challenges over the decades. Worldwide, many nations are struggling to conserve and to bridge the gap between the demand and production of the resource. Though many measures like several Government acts, replacing existing products with energy conserving products and many solar based systems are being invented and used in practise, the demand and the need to preserve the resource still persists. Hence, this paper focuses on a novel technique to conserve the electric resource using a deep learning technique. The system uses Convolutional Neural Networks to identify and localize humans in the CCTV footages using EfficientNet, a deep transfer learning model. The classifier processes and yields its output to an embedded Arduino microcontroller, after detecting the presence/absence of human. The microcontroller enables/disables the electric power supply in the area of human’s presence/absence, based on the classifier’s output respectively. The system achieves an accuracy percentage of 84.2% in detecting humans in the footages with the subsequent enabling/disabling of electric power resource to conserve electricity.

scholarly journals Optimization of Regional - Convolutional Neural Network for Electricity Conservation Using Arduino

Webology ◽  
2021 ◽  
Vol 18 (SI01) ◽  
pp. 32-46
Author(s):  
S. Ushasukhanya ◽  
S. Jothilakshmi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electric Power ◽  
Power Supply ◽  
Electrical Energy ◽  
Electric Power Supply ◽  
Surveillance Camera ◽  
Cctv Surveillance ◽  
Electricity Conservation ◽  
Electric Supply

The demand for electrical energy in developing countries is apparently increasing thereby creating a large gap between the availability of the electrical resource and its growing demand. Globally reputed energy economists have recognized that 25% of reduction in energy consumption can be achieved by adopting efficient energy conservation techniques This paper presents one of the simplest ways of conservation techniques that enables the electric power supply only when it is actually needed. It is an automatic system that functions with the existing CCTV surveillance camera to enable/disable the electric power supply, only in the location where human is present / absent respectively. The proposed approach is demonstrated without the use of sensors, based on Regional Convolutional Neural Network (R-CNN). A new R-CNN model is constructed for CHOKEPOINT dataset and the optimization is done using Nadam technique. The results are then fed into Arduino micro controller to control the electric supply based on the presence/absence of human in the particular region.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

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