Show simple item record

dc.contributor.authorRahmat, Romi Fadillah
dc.contributor.authorDennis
dc.contributor.authorSitompul, Opim Salim
dc.contributor.authorPurnamawati, Sarah
dc.contributor.authorBudiarto, Rahmat
dc.date.accessioned2020-02-10T08:46:21Z
dc.date.available2020-02-10T08:46:21Z
dc.date.issued2019
dc.identifier.otherMuhammad Salim
dc.identifier.urihttp://repository.usu.ac.id/handle/123456789/70879
dc.descriptionRomi Fadillah Rahmatid
dc.description.abstractIn 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.id
dc.language.isoenid
dc.publisherUniversitas Sumatera Utaraid
dc.subjectAdvertisement billboard detectionid
dc.subjectAdvertisement billboard detectionid
dc.subjectDeep convolutional neural networkid
dc.subjectImage classificationid
dc.subjectTransfer learningid
dc.titleAdvertisement Billboard Detection and Geotagging System With Inductive Transfer Learning in Deep Convolutional Neural Networkid
dc.typeLecture Papersid


Files in this item

Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record