Object detection using convolutional neural networks

Vision systems are essential in building a mobile robot that will complete a certain task like navigation, surveillance, and explosive ordnance disposal (EOD). This will make the robot controller or the operator aware what is in the environment and perform the next tasks. With the recent advancement...

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Main Authors: Galvez, Reagan L., Bandala, Argel A., Dadios, Elmer P., Vicerra, Ryan Rhay P., Maningo, Jose Martin Z.
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Published: Animo Repository 2019
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2923
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-39222021-11-16T08:41:17Z Object detection using convolutional neural networks Galvez, Reagan L. Bandala, Argel A. Dadios, Elmer P. Vicerra, Ryan Rhay P. Maningo, Jose Martin Z. Vision systems are essential in building a mobile robot that will complete a certain task like navigation, surveillance, and explosive ordnance disposal (EOD). This will make the robot controller or the operator aware what is in the environment and perform the next tasks. With the recent advancement in deep neural networks in image processing, classifying and detecting the object accurately is now possible. In this paper, Convolutional Neural Networks (CNN) is used to detect objects in the environment. Two state of the art models are compared for object detection, Single Shot Multi-Box Detector (SSD) with MobileNetV1 and a Faster Region-based Convolutional Neural Network (Faster-RCNN) with InceptionV2. Result shows that one model is ideal for real-time application because of speed and the other can be used for more accurate object detection. © 2018 IEEE. 2019-02-22T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2923 Faculty Research Work Animo Repository Computer vision Image processing Image converters Neural networks (Computer science) Electrical and Computer Engineering Electrical and Electronics
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Computer vision
Image processing
Image converters
Neural networks (Computer science)
Electrical and Computer Engineering
Electrical and Electronics
spellingShingle Computer vision
Image processing
Image converters
Neural networks (Computer science)
Electrical and Computer Engineering
Electrical and Electronics
Galvez, Reagan L.
Bandala, Argel A.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
Maningo, Jose Martin Z.
Object detection using convolutional neural networks
description Vision systems are essential in building a mobile robot that will complete a certain task like navigation, surveillance, and explosive ordnance disposal (EOD). This will make the robot controller or the operator aware what is in the environment and perform the next tasks. With the recent advancement in deep neural networks in image processing, classifying and detecting the object accurately is now possible. In this paper, Convolutional Neural Networks (CNN) is used to detect objects in the environment. Two state of the art models are compared for object detection, Single Shot Multi-Box Detector (SSD) with MobileNetV1 and a Faster Region-based Convolutional Neural Network (Faster-RCNN) with InceptionV2. Result shows that one model is ideal for real-time application because of speed and the other can be used for more accurate object detection. © 2018 IEEE.
format text
author Galvez, Reagan L.
Bandala, Argel A.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
Maningo, Jose Martin Z.
author_facet Galvez, Reagan L.
Bandala, Argel A.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
Maningo, Jose Martin Z.
author_sort Galvez, Reagan L.
title Object detection using convolutional neural networks
title_short Object detection using convolutional neural networks
title_full Object detection using convolutional neural networks
title_fullStr Object detection using convolutional neural networks
title_full_unstemmed Object detection using convolutional neural networks
title_sort object detection using convolutional neural networks
publisher Animo Repository
publishDate 2019
url https://animorepository.dlsu.edu.ph/faculty_research/2923
_version_ 1718382714423345152