Rahmat Budiarto
College of Computer Science and Information Technology, Albaha University, Saudi Arabia

Published : 13 Documents
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Journal : TELKOMNIKA Telecommunication, Computing, Electronics and Control

Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network Rahmat, Romi Fadillah; Dennis, Dennis; Sitompul, Opim Salim; Purnamawati, Sarah; Budiarto, Rahmat
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1056.819 KB) | DOI: 10.12928/telkomnika.v17i5.11276

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.