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This study presents sheep detection and quantification methods for video frames captured by UAVs and examinate their advantages over traditional UAV video recording. The implementation of two digital image processing approaches for pattern recognition is based on color-shape analysis and the YOLOv5 classifier to detect sheep in a field from a video source. Both methods are implemented in Python 3 using OpenCV tools, where morphological operations and feature classification algorithms are applied. These methods perform multiple operations on the video source and have proven to be effective for detecting sheep images under different lighting conditions, as well as at varying distances between the UAV and the sheep.

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Drone-Based Sheep Recognition and Counting: A Comparative Study of Color-Shape, and YOLOv5 Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Juan Ronaldo Condori Ticona,
  • Gavino José Flores Chipana,
  • Guido Humberto Cayo Cabrera,
  • Teobaldo Raúl Basurco Chambilla,
  • Gabino Rey Vidangos Ponce

摘要

This study presents sheep detection and quantification methods for video frames captured by UAVs and examinate their advantages over traditional UAV video recording. The implementation of two digital image processing approaches for pattern recognition is based on color-shape analysis and the YOLOv5 classifier to detect sheep in a field from a video source. Both methods are implemented in Python 3 using OpenCV tools, where morphological operations and feature classification algorithms are applied. These methods perform multiple operations on the video source and have proven to be effective for detecting sheep images under different lighting conditions, as well as at varying distances between the UAV and the sheep.