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La fusión de transformación Gaussian-Haar mejora el DEIM para la detección de madurez de la granada
Yuying Wang1, Songzuo Liu2,3, Pingping Hao1
1Information Engineering College, Shandong Huayu University of Technology, Dezhou, 253000, China.
Scientific reports
|February 11, 2026
Resumen
Este estudio introduce GLMF-DEIM, un nuevo algoritmo para la detección automatizada de la madurez de la granada. Mejora la precisión en entornos complejos y ofrece computación eficiente para aplicaciones de agricultura inteligente.
Área de la Ciencia:
- Tecnología Agrícola Tecnología agrícola.
- Visión por ordenador Visión por ordenador Visión por ordenador Visión por ordenador Visión por ordenador
- Aprendizaje automático Aprendizaje automático.
Sus antecedentes:
- La detección automatizada de la madurez de la granada es crucial para optimizar el rendimiento y reducir los costos.
- Los métodos existentes luchan por distinguir las granadas de la follaje y equilibrar la precisión con la eficiencia computacional en entornos naturales.
Objetivo del estudio:
- Desarrollar un algoritmo ligero de detección de madurez de la granada, GLMF-DEIM, que supere las limitaciones de los enfoques actuales.
- Para mejorar la precisión de detección y la eficiencia computacional para aplicaciones agrícolas en el mundo real.
Principales métodos:
- El algoritmo GLMF-DEIM utiliza un módulo de transformación de ondulaciones discretas de Gauss-Haar para la separación de características.
- Incorpora el Downsampling de peso adaptativo ligero y las etapas de convolución dinámica de dominio de frecuencia ligera para una extracción de características eficiente.
- Se emplea una red de fusión de características de varios niveles y estrategias de capacitación optimizadas (combinación de densidad O2O, pérdida de conocimiento de compatibilidad).
Principales resultados:
- El modelo GLMF-DEIM logró métricas de alto rendimiento: 93.1% AP50, 84.5% AP75 y 32.7% APS en un conjunto de datos de 5.855 imágenes.
- Se demostraron mejoras relativas de 1.9%, 2.3% y 1.3% respecto a la línea de base óptima.
- Se logró un bajo costo computacional (16,9 GFLOPs) con 8,16 millones de parámetros, lo que indica una inferencia eficiente.
Conclusiones:
- GLMF-DEIM aborda efectivamente la similitud entre el objetivo y el fondo y logra una compensación favorable entre precisión y eficiencia.
- El algoritmo es muy adecuado para el despliegue de borde en sistemas de agricultura inteligente para el monitoreo de granada en tiempo real.
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