Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning

The current system to detect the bottle caps is using the HSV (Hue, Saturation and Value) technique. This technique has an accuracy of 85.938% to count the bottle caps. The accuracy of the system to count the bottle caps can be increased by implementing computer vision with deep learning. The...

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Main Author: Saifbudin, Abdul Syahid
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.20879 /
Published: IRC 2019
Subjects:
Q Science (General)
Online Access: http://utpedia.utp.edu.my/20879/1/Abdul%20Syahid_23033.pdf
http://utpedia.utp.edu.my/20879/
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id utp-utpedia.20879
recordtype eprints
spelling utp-utpedia.208792021-09-09T19:58:11Z http://utpedia.utp.edu.my/20879/ Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning Saifbudin, Abdul Syahid Q Science (General) The current system to detect the bottle caps is using the HSV (Hue, Saturation and Value) technique. This technique has an accuracy of 85.938% to count the bottle caps. The accuracy of the system to count the bottle caps can be increased by implementing computer vision with deep learning. The proposed system should be able improve the accuracy to count the bottle caps. The development of the system is develop using python, computer vision and deep learning. The output of the result is expected to improve the accuracy for the detection of the bottle cap by 15 percent. IRC 2019-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20879/1/Abdul%20Syahid_23033.pdf Saifbudin, Abdul Syahid (2019) Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic Q Science (General)
spellingShingle Q Science (General)
Saifbudin, Abdul Syahid
Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning
description The current system to detect the bottle caps is using the HSV (Hue, Saturation and Value) technique. This technique has an accuracy of 85.938% to count the bottle caps. The accuracy of the system to count the bottle caps can be increased by implementing computer vision with deep learning. The proposed system should be able improve the accuracy to count the bottle caps. The development of the system is develop using python, computer vision and deep learning. The output of the result is expected to improve the accuracy for the detection of the bottle cap by 15 percent.
format Final Year Project
author Saifbudin, Abdul Syahid
author_sort Saifbudin, Abdul Syahid
title Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning
title_short Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning
title_full Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning
title_fullStr Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning
title_full_unstemmed Improving the Accuracy to Count Bottle Caps by Implementation of Computer Vision and Deep Learning
title_sort improving the accuracy to count bottle caps by implementation of computer vision and deep learning
publisher IRC
publishDate 2019
url http://utpedia.utp.edu.my/20879/1/Abdul%20Syahid_23033.pdf
http://utpedia.utp.edu.my/20879/
_version_ 1741195676442689536
score 11.62408

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