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...
| Main Author: | Saifbudin, Abdul Syahid |
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| Format: | Final Year Project |
| Language: | English |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-utpedia.20879 / |
| Published: |
IRC
2019
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| Subjects: | |
| Online Access: |
http://utpedia.utp.edu.my/20879/1/Abdul%20Syahid_23033.pdf http://utpedia.utp.edu.my/20879/ |
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| Summary: |
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. |
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