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Colaboración humano-máquina consciente de la incertidumbre en la detección de objetos camuflados

Ziyue Yang1,2, Kehan Wang1,2, Yuhang Ming1,2

  • 1School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.

Cognitive neurodynamics
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Este estudio presenta una novedosa colaboración humano-máquina para la detección de objetos camuflados (COD), que integra la visión por computadora con interfaces cerebro-computadora (BCI) para mejorar la precisión y la eficiencia en la identificación de objetos ocultos.

Palabras clave:
Interfaz cerebro-computadoraDetección de objetos camufladosVisión por computadoraColaboración humano-máquinaEstimación de incertidumbre

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Área de la Ciencia:

  • Visión por Computadora
  • Interacción Humano-Computadora
  • Ingeniería Biomédica

Sus antecedentes:

  • La detección de objetos camuflados (COD) es crucial para aplicaciones que requieren la identificación de objetos ocultos.
  • Los métodos existentes a menudo tienen dificultades con el camuflaje complejo, lo que requiere una mayor fiabilidad y eficiencia.
  • La colaboración humano-máquina ofrece una vía prometedora para aprovechar las fortalezas complementarias.

Objetivo del estudio:

  • Desarrollar un novedoso marco de colaboración humano-máquina para la detección de objetos camuflados (COD).
  • Mejorar el rendimiento de COD integrando modelos de visión por computadora (CV) con interfaces cerebro-computadora (BCI) no invasivas.
  • Mejorar la fiabilidad del sistema y reducir la carga cognitiva humana en tareas de detección complejas.

Principales métodos:

  • Se propuso una arquitectura de referencia multivista para estimar la incertidumbre en las predicciones de CV.
  • Se utilizó la estimación de incertidumbre durante el entrenamiento para aumentar la eficiencia.
  • Se implementó un sistema de evaluación humana utilizando BCI basadas en RSVP para predicciones de baja confianza durante las pruebas.

Principales resultados:

  • Se obtuvieron resultados de vanguardia en el conjunto de datos CAMO, mejorando la precisión balanceada (BA) en un 4,56% y la puntuación F1 en un 3,66%.
  • Se demostraron mejoras significativas de rendimiento para los principales participantes (hasta un 7,6% de BA, 6,66% de F1).
  • Se confirmó una fuerte correlación entre la confianza del modelo y la precisión de la detección.

Conclusiones:

  • El marco propuesto mejora eficazmente la detección de objetos camuflados a través de la colaboración humano-máquina.
  • La integración de CV consciente de la incertidumbre y BCI mejora la fiabilidad y la eficiencia del sistema.
  • Este enfoque proporciona una base para aplicaciones avanzadas de COD en el mundo real y la interacción humano-computadora.