Weight Estimation of Broilers in Images Using 3D Prior Knowledge

Anders Jørgensen*, Jacob V. Dueholm, Jens Fagertun, Thomas B. Moeslund

*Corresponding author

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)


Cameras are already widely used for inspection and monitoring tasks in poultry slaughter houses. In this paper we evaluate the use of computer vision for broiler carcass weight estimation. We compare the use of 2D image features with 3D features extracted from a statistical shape model fitted to the image. The statistical shape model is built from 45 3D scans captured from broiler carcasses collected at a slaughter house. The use of this 3D prior gave a reduction in mean absolute error compared to 2D features alone and achieved an overall mean average percentage error of 3.47%. The algorithm can run real time and was tested on a dataset containing 136,472 images of broilers, captured at a real production site.
Original languageEnglish
Title of host publicationImage Analysis : 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings
EditorsMichael Felsberg, Per-Erik Forssén, Jonas Unger, Ida-Maria Sintorn
Number of pages12
Publication date1 Jan 2019
ISBN (Print)978-3-030-20204-0
ISBN (Electronic)978-3-030-20205-7
Publication statusPublished - 1 Jan 2019
Event21st Scandinavian Conference on Image Analysis, SCIA 2019 - Norrköping, Sweden
Duration: 11 Jun 201913 Jun 2019


Conference21st Scandinavian Conference on Image Analysis, SCIA 2019
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11482 LNCS


  • 3D prior knowledge
  • Broiler
  • Model fitting
  • Statistical shape model
  • Weight estimation


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