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The important convolution properties include width, area, differentiation, and integration properties.
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Método de detección de defectos superficiales en aluminio aeroespacial basado en convolución multiescala y mecanismo

Rui Zhang1, Shanshan Cai2, Zhen He1

  • 1School of Engineering Science, Shandong Xiehe University, Jinan, 250107, China.

Scientific reports
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Este estudio presenta un método mejorado para detectar pequeños defectos en superficies de aluminio utilizando un marco YOLOv11n mejorado. El nuevo enfoque aumenta significativamente la precisión de la detección y el rendimiento del reconocimiento para aplicaciones industriales críticas.

Palabras clave:
Detección de defectos superficiales en aluminioOperador de remuestreo CARAFEMódulo de bloque de reparametrización dilatada y residual dilatadaMecanismo de atención SimAMYOLOv11n

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Área de la Ciencia:

  • Ciencia de los materiales
  • Visión por computadora
  • Tecnología de fabricación

Sus antecedentes:

  • Los defectos de objetivos pequeños en las superficies de aluminio suponen un riesgo importante para la seguridad y la durabilidad del producto en industrias clave.
  • Los métodos de detección tradicionales se enfrentan a desafíos con la diversidad de defectos, el tamaño pequeño, el bajo contraste y el ruido de fondo.

Objetivo del estudio:

  • Desarrollar un método mejorado de detección de objetivos pequeños para superficies de aluminio.
  • Mejorar la precisión y la fiabilidad de la detección de defectos en la fabricación industrial.

Principales métodos:

  • Se aprovechó el marco YOLOv11n para la detección de objetos.
  • Se integró un bloque de reparametrización dilatada y residual dilatada para la extracción de características.
  • Se incorporó el mecanismo de atención SimAM para un enfoque mejorado en características críticas.
  • Se utilizó el operador de remuestreo CARAFE para preservar los detalles de los objetivos pequeños.

Principales resultados:

  • Se logró una precisión media promedio (mAP@0.5) del 79,4%, una mejora del 2,9% con respecto a la línea de base.
  • Se alcanzó una recuperación del 76,6%, una mejora del 4,4% con respecto a la línea de base.
  • Se demostró una precisión de detección y una capacidad de reconocimiento superiores en comparación con los métodos existentes.

Conclusiones:

  • El método mejorado YOLOv11n propuesto detecta eficazmente pequeños defectos en superficies de aluminio.
  • La integración de módulos novedosos mejora significativamente la extracción de características y los mecanismos de atención.
  • Este enfoque ofrece una solución robusta para escenarios prácticos de detección de defectos industriales.