Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network
Romi Fadillah Rahmat, Dennis Dennis, Opim Salim Sitompul, Sarah Purnamawati, Rahmat Budiarto
Abstract
In this paper, we propose an approach to detect and geotag advertisement billboard in real-time condition. Our approach is using AlexNet’s Deep Convolutional Neural Network (DCNN) as a pre-trained neural network with 1000 categories for image classification. To improve the performance of the pre-trained neural network, we retrain the network by adding more advertisement billboard images using inductive transfer learning approach. Then, we fine-tuned the output layer into advertisement billboard related categories. Furthermore, the detected advertisement billboard images will be geotagged by inserting Exif metadata into the image file. Experimental results show that the approach achieves 92.7% training accuracy for advertisement billboard detection, while for overall testing results it will give 71,86% testing accuracy.
Keywords
advertisement billboard detection; advertisement billboard geotagging; deep convolutional neural network; image classification; transfer learning;
DOI:
http://doi.org/10.12928/telkomnika.v17i5.11276
Refbacks
There are currently no refbacks.
This work is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License .
TELKOMNIKA Telecommunication, Computing, Electronics and Control ISSN: 1693-6930, e-ISSN: 2302-9293Universitas Ahmad Dahlan , 4th Campus Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191 Phone: +62 (274) 563515, 511830, 379418, 371120 Fax: +62 274 564604
<div class="statcounter"><a title="Web Analytics" href="http://statcounter.com/" target="_blank"><img class="statcounter" src="//c.statcounter.com/10241713/0/0b6069be/0/" alt="Web Analytics"></a></div> View TELKOMNIKA Stats