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Un algoritmo de detección de objetivos mejorado basado en YOLOv8s

Xinwei Wang1, Yue Hu1, Qing Liang1

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PubMed
Resumen
Este resumen es generado por máquina.

Este estudio mejora la detección de objetivos de drones utilizando un algoritmo de aprendizaje profundo, mejorando la precisión y la eficiencia para la economía de baja altitud. El nuevo método aumenta las tasas de detección al tiempo que reduce el tamaño del modelo para una mejor percepción de los UAV.

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Área de la Ciencia:

  • Visión por computadora
  • Inteligencia artificial
  • Ingeniería Aeroespacial

Sus antecedentes:

  • El rápido crecimiento de la economía de baja altitud requiere operaciones avanzadas de vehículos aéreos no tripulados (UAV).
  • Los UAV requieren una sólida percepción ambiental y medidas de seguridad para una navegación segura en un espacio aéreo complejo.
  • Los algoritmos de detección de objetivos existentes como YOLOv8 se enfrentan a limitaciones en el procesamiento a escala múltiple y la detección de objetivos pequeños para aplicaciones de UAV.

Objetivo del estudio:

  • Desarrollar un algoritmo de detección de objetivos mejorado basado en el aprendizaje profundo para los VANT.
  • Mejorar la precisión y la velocidad de detección para la percepción autónoma de UAV en la economía de baja altitud.
  • Para abordar las limitaciones de YOLOv8s en la extracción de características a escala múltiple y la identificación de objetivos pequeños.

Principales métodos:

  • Se introdujo AKConv en el módulo C2F para operaciones de convolución adaptativa y extracción eficiente de características.
  • Integró el mecanismo LSKA en el módulo SPPF para mejorar la extracción de características de destino pequeñas y la captura de dependencias de largo alcance.
  • Propuso una nueva red piramidal de características Bi-SCDown-FPN para la fusión acelerada y enriquecida de características en el cuello del modelo.

Principales resultados:

  • El algoritmo mejorado logró un aumento del 5,9% en la precisión de detección, del 4,5% en el recuerdo de detección y del 6,1% en la precisión promedio del conjunto de datos VisDrone2019.
  • Se redujo el número de parámetros en un 13,41% y el tamaño del archivo de peso en un 13,33%, lo que indica la ligereza del modelo.
  • Demostró un rendimiento superior en comparación con otros algoritmos de detección de objetivos convencionales.

Conclusiones:

  • El algoritmo propuesto ofrece una doble mejora en la ligereza del modelo y la precisión de detección para los UAV.
  • Las mejoras permiten una percepción autónoma más eficiente y precisa para los drones en entornos complejos.
  • Este avance apoya la operación segura y ordenada de los UAV dentro de la floreciente economía de baja altitud.