Iterative Bounding Box Annotation for Object Detection

Bishwo Adhikari, Heikki Huttunen

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

23 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
Otsikko2020 25th International Conference on Pattern Recognition (ICPR)
KustantajaIEEE
Sivut4040-4046
Sivumäärä7
ISBN (elektroninen)978-1-7281-8808-9
ISBN (painettu)978-1-7281-8809-6
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Pattern Recognition - Milan, Italia
Kesto: 10 tammik. 202115 tammik. 2021

Julkaisusarja

NimiInternational Conference on Pattern Recognition
ISSN (painettu)1051-4651

Conference

ConferenceInternational Conference on Pattern Recognition
Maa/AlueItalia
KaupunkiMilan
Ajanjakso10/01/2115/01/21

Julkaisufoorumi-taso

  • Jufo-taso 1

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