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


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.


advertisement billboard detection; advertisement billboard geotagging; deep convolutional neural network; image classification; transfer learning;


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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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