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Detection of Cattle Using Drones and Convolutional Neural Networks.

Alberto Rivas1, Pablo Chamoso2, Alfonso González-Briones3

  • 1BISITE Digital Innovation Hub, University of Salamanca, Edificio Multiusos I+D+i, Calle Espejo 2, 37007 Salamanca, Spain. rivis@usal.es.

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Summary

Multirotor drones equipped with artificial intelligence can now detect cattle in real-time. This study details a platform using Convolutional Neural Networks (CNNs) for image analysis, showcasing drone technology

Keywords:
Unmanned Aerial Vehiclecattle detectionconvolutional neural networkdronemultirotor

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Agricultural Technology

Background:

  • Multirotor drones represent a significant technological advancement with versatile flight capabilities, including vertical takeoff.
  • Advances in computing and telecommunications have expanded drone applications across various professional fields.
  • Artificial intelligence and information analysis are key research areas driving innovation.

Purpose of the Study:

  • To design and evaluate a platform for real-time information analysis using drones.
  • To apply artificial intelligence techniques, specifically Convolutional Neural Networks (CNNs), for object detection in drone-captured imagery.
  • To demonstrate the efficacy of CNNs trained for cattle detection, with potential for broader object recognition applications.

Main Methods:

  • Development of a drone-based platform for real-time data acquisition.
  • Implementation of Convolutional Neural Networks (CNNs) for image analysis.
  • Training a CNN model specifically for the identification and detection of cattle in images captured by the drone's camera.

Main Results:

  • The study successfully designed a platform for real-time analysis of drone-collected information.
  • A CNN model was trained and demonstrated effective performance in detecting cattle within the captured images.
  • The methodology allows for the potential adaptation of the CNN for detecting other objects.

Conclusions:

  • The integration of AI, particularly CNNs, with multirotor drones enables efficient real-time object detection.
  • The developed platform and trained CNN model show promise for agricultural applications, such as livestock monitoring.
  • The approach is adaptable for training CNNs to detect a wide range of objects, highlighting the versatility of the system.