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Scrap-SAM-CLIP: Ensamblaje de Modelos Fundacionales para el Reconocimiento Típico de Formas en la Clasificación y

Guangda Bao1, Wenzhi Xia1, Haichuan Wang1,2

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Resumen

Este estudio presenta Scrap-SAM-CLIP (SSC), un modelo de visión y lenguaje para la inferencia precisa de dimensiones 3D de desechos. SSC mejora los sistemas de visión industrial al mejorar las capacidades de identificación de formas y reconstrucción 3D de desechos.

Palabras clave:
solución de reconocimiento 3Dpreentrenamiento contrastivo de lenguaje-imagenajuste finoidentificación de desechosmodelo de segmentación de todo

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Área de la Ciencia:

  • Visión por Computadora
  • Inteligencia Artificial
  • Automatización Industrial

Sus antecedentes:

  • Los métodos 2D tienen dificultades para inferir dimensiones absolutas de desechos a partir de imágenes.
  • El reconocimiento 3D preciso requiere una identificación robusta de formas de desechos y extracción dimensional.

Objetivo del estudio:

  • Desarrollar un modelo de visión y lenguaje para la inferencia precisa de dimensiones 3D de desechos.
  • Establecer un marco fundacional para la reconstrucción 3D y la extracción dimensional en la identificación industrial de desechos.

Principales métodos:

  • Se propuso Scrap-SAM-CLIP (SSC), que integra el Modelo de Segmentación de Todo (SAM) y el Preentrenamiento Contrastivo de Lenguaje-Imagen Chino (CN-CLIP).
  • Se ajustaron módulos individuales de SSC en un conjunto de datos de desechos autoconstruido.
  • Se evaluaron varias estrategias de indicaciones para la segmentación, y las indicaciones combinadas de cuadro y punto mostraron un rendimiento óptimo.

Principales resultados:

  • SSC logró una precisión del 95,3% en la clasificación, superando a Swin Transformer V2_base en un 2,9%.
  • El ajuste fino del decodificador SAM mejoró la robustez bajo indicaciones ruidosas, aumentando la precisión hasta en un 15%.
  • Las visualizaciones t-SNE confirmaron las capacidades superiores de aprendizaje de características de SSC.

Conclusiones:

  • La estrategia de ensamblaje modular de SSC permite la optimización específica de componentes y mejora la interpretabilidad del sistema.
  • Los modelos fundacionales adaptados resultan eficaces para los sistemas de visión industrial, refinando el proceso de identificación 3D de desechos.