Aktif Öǧrenme Yöntemi Kullanarak Nesne Tespiti

Translated title of the contribution: Aktif Öǧrenme Yöntemi Kullanarak Nesne Tespiti Object Detection Using Active Learning

Nuh Hatipoglu, Esra Cinar, Hazim Kemal Ekenel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the last decade, deep learning-based object detection models have achieved high performance. However, to train these object detection models, a large amount of labeled images is required. Active learning is a machine learning procedure that is useful in reducing the amount of labeled data required to achieve the targeted performance. With active learning, it is possible to obtain high performing models on real-world data where annotation is time-consuming, while decreasing the labeling cost. It helps reduce the cost of data labeling by efficiently selecting a subset of informative samples from a large repository of unlabeled data. In this study, we developed an object detection model combined with active learning. The results of the experiments show that almost the same level of success was achieved by labeling a smaller amount of data with the active learning framework, compared to labeling and using all the data, leading to lower labeling costs.

Translated title of the contributionAktif Öǧrenme Yöntemi Kullanarak Nesne Tespiti Object Detection Using Active Learning
Original languageTurkish
Title of host publication2022 30th Signal Processing and Communications Applications Conference, SIU 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665450928
DOIs
Publication statusPublished - 2022
Event30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey
Duration: 15 May 202218 May 2022

Publication series

Name2022 30th Signal Processing and Communications Applications Conference, SIU 2022

Conference

Conference30th Signal Processing and Communications Applications Conference, SIU 2022
Country/TerritoryTurkey
CitySafranbolu
Period15/05/2218/05/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

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