Automatic object detection for behavioural research using YOLOv8

Frouke Hermens*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO (‘you only look once’) and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings.

Original languageEnglish
Number of pages24
JournalBehavior Research Methods
DOIs
Publication statusE-pub ahead of print - 15 May 2024

Keywords

  • Automatic object detection
  • Behavioural analysis
  • Surgical tool tracking
  • YOLO

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