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 contribution | Aktif Öǧrenme Yöntemi Kullanarak Nesne Tespiti Object Detection Using Active Learning |
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Original language | Turkish |
Title of host publication | 2022 30th Signal Processing and Communications Applications Conference, SIU 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665450928 |
DOIs | |
Publication status | Published - 2022 |
Event | 30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey Duration: 15 May 2022 → 18 May 2022 |
Publication series
Name | 2022 30th Signal Processing and Communications Applications Conference, SIU 2022 |
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Conference
Conference | 30th Signal Processing and Communications Applications Conference, SIU 2022 |
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Country/Territory | Turkey |
City | Safranbolu |
Period | 15/05/22 → 18/05/22 |
Bibliographical note
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