Iterative Bounding Box Annotation for Object Detection

Bishwo Adhikari, Heikki Huttunen

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Manual annotation of bounding boxes for object detection in digital images is tedious, and time and resource consuming. In this paper, we propose a semi-automatic method for efficient bounding box annotation. The method trains the object detector iteratively on small batches of labeled images and learns to propose bounding boxes for the next batch, after which the human annotator only needs to correct possible errors. We propose an experimental setup for simulating the human actions and use it for comparing different iteration strategies, such as the order in which the data is presented to the annotator. We experiment on our method with three datasets and show that it can reduce the human annotation effort significantly, saving up to 75% of total manual annotation work.
Original languageEnglish
Title of host publication2020 25th International Conference on Pattern Recognition (ICPR)
Number of pages7
ISBN (Electronic)978-1-7281-8808-9
ISBN (Print)978-1-7281-8809-6
Publication statusPublished - 2021
Publication typeA4 Article in a conference publication
EventInternational Conference on Pattern Recognition - Milan, Italy
Duration: 10 Jan 202115 Jan 2021

Publication series

NameInternational Conference on Pattern Recognition
ISSN (Print)1051-4651


ConferenceInternational Conference on Pattern Recognition


  • Bounding boxes
  • image annotation
  • iterative annotation
  • object detection
  • deep learning

Publication forum classification

  • Publication forum level 1


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