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EBSnoR: Eliminación de nieve basada en eventos mediante un umbral de tiempo de permanencia óptimo
IEEE transactions on pattern analysis and machine intelligence
|August 28, 2025
Resumen
Desarrollamos EBSnoR, un algoritmo de eliminación de nieve basado en eventos. Identifica con precisión los copos de nieve utilizando el tiempo de permanencia de los píxeles, mejorando la detección de objetos en condiciones de nieve con un 96,19% de precisión.
Área de la Ciencia:
- Visión por computadora
- La robótica
- Tecnología de sensores
Sus antecedentes:
- Las cámaras basadas en eventos ofrecen alta resolución temporal y baja latencia, ideal para escenas dinámicas.
- Las nevadas presentan desafíos significativos para los sistemas tradicionales de visión por computadora debido a la oclusión y el ruido.
- Las técnicas de eliminación de nieve existentes a menudo no están optimizadas para datos basados en eventos.
Objetivo del estudio:
- Para introducir EBSnoR, un nuevo algoritmo de eliminación de nieve basado en eventos.
- Permitir una detección robusta de objetos en condiciones climáticas adversas utilizando sensores basados en eventos.
- Evaluar el rendimiento de EBSnoR en conjuntos de datos de nieve reales y simulados.
Principales métodos:
- Desarrolló una técnica para medir el tiempo de permanencia del copo de nieve en píxeles utilizando datos de la cámara basados en eventos.
- Implementación de umbrales de tiempo de permanencia estadísticamente óptimos para diferenciar los eventos de copo de nieve del ruido de fondo.
- Validar el algoritmo cualitativamente en el conjunto de datos UDayton25EBSnow y cuantitativamente utilizando el simulador EBSnoGen.
Principales resultados:
- EBSnoR identifica efectivamente los eventos que corresponden a los copos de nieve.
- El algoritmo logró una precisión cuantitativa del 96,19% en la remoción de nieve.
- La remoción de nieve utilizando EBSnoR demostró un mejor rendimiento en tareas posteriores de detección de objetos basadas en eventos.
Conclusiones:
- EBSnoR es un método altamente preciso y eficaz para la remoción de nieve en sistemas de visión basados en eventos.
- La técnica propuesta mejora significativamente la fiabilidad de la detección de objetos en entornos nevados.
- Este trabajo abre nuevas posibilidades para los sistemas autónomos que operan en condiciones climáticas difíciles.
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