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Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks

Sensors (Basel, Switzerland), 2022-07, Vol.22 (14), p.5188 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22145188 ;PMID: 35890870

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  • Title:
    Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks
  • Author: Volkmann, Nina ; Zelenka, Claudius ; Devaraju, Archana Malavalli ; Brünger, Johannes ; Stracke, Jenny ; Spindler, Birgit ; Kemper, Nicole ; Koch, Reinhard
  • Subjects: Animal welfare ; crowded dataset ; Datasets ; Husbandry ; Image segmentation ; injury location ; keypoint detection ; Labels ; Neural networks ; Pose estimation ; Position (location) ; Poultry ; Software ; Turkeys
  • Is Part Of: Sensors (Basel, Switzerland), 2022-07, Vol.22 (14), p.5188
  • Description: Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using “near tail” or “near head” labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s22145188
    PMID: 35890870
  • Source: DOAJ Directory of Open Access Journals
    GFMER Free Medical Journals
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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