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High-precision tracking and positioning for monitoring Holstein cattle.
Luo, Wei; Zhang, Guoqing; Yuan, Quanbo; Zhao, Yongxiang; Chen, Hongce; Zhou, Jingjie; Meng, Zhaopeng; Wang, Fulong; Li, Lin; Liu, Jiandong; Wang, Guanwu; Wang, Penggang; Yu, Zhongde.
Afiliación
  • Luo W; North China Institute of Aerospace Engineering, Langfang, China.
  • Zhang G; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang, China.
  • Yuan Q; National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang, China.
  • Zhao Y; North China Institute of Aerospace Engineering, Langfang, China.
  • Chen H; North China Institute of Aerospace Engineering, Langfang, China.
  • Zhou J; College of Intelligence and Computing, Tianjin University, Tianjin, China.
  • Meng Z; North China Institute of Aerospace Engineering, Langfang, China.
  • Wang F; North China Institute of Aerospace Engineering, Langfang, China.
  • Li L; College of Intelligence and Computing, Tianjin University, Tianjin, China.
  • Liu J; Tellyes Scientific Inc. Tianjin, China.
  • Wang G; College of Intelligence and Computing, Tianjin University, Tianjin, China.
  • Wang P; North China Institute of Aerospace Engineering, Langfang, China.
  • Yu Z; North China Institute of Aerospace Engineering, Langfang, China.
PLoS One ; 19(5): e0302277, 2024.
Article en En | MEDLINE | ID: mdl-38743665
ABSTRACT
Enhanced animal welfare has emerged as a pivotal element in contemporary precision animal husbandry, with bovine monitoring constituting a significant facet of precision agriculture. The evolution of intelligent agriculture in recent years has significantly facilitated the integration of drone flight monitoring tools and innovative systems, leveraging deep learning to interpret bovine behavior. Smart drones, outfitted with monitoring systems, have evolved into viable solutions for wildlife protection and monitoring as well as animal husbandry. Nevertheless, challenges arise under actual and multifaceted ranch conditions, where scale alterations, unpredictable movements, and occlusions invariably influence the accurate tracking of unmanned aerial vehicles (UAVs). To address these challenges, this manuscript proposes a tracking algorithm based on deep learning, adhering to the Joint Detection Tracking (JDT) paradigm established by the CenterTrack algorithm. This algorithm is designed to satisfy the requirements of multi-objective tracking in intricate practical scenarios. In comparison with several preeminent tracking algorithms, the proposed Multi-Object Tracking (MOT) algorithm demonstrates superior performance in Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), and IDF1. Additionally, it exhibits enhanced efficiency in managing Identity Switches (ID), False Positives (FP), and False Negatives (FN). This algorithm proficiently mitigates the inherent challenges of MOT in complex, livestock-dense scenarios.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos Límite: Animals Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos Límite: Animals Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos