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Structural Classification of Joints

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Algoritmo inteligente de detección de objetivos de vehículos basado en características de escala múltiple

Aijuan Li1, Xiangsen Ning1, Máté Zöldy2

  • 1School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio optimiza el modelo de detección de objetos YOLOv10 para la conducción inteligente, reduciendo su tamaño en un 11,8% y logrando una precisión del 93,0%. El modelo mejorado ofrece un rendimiento de detección mejorado para los sistemas autónomos.

Palabras clave:
YOLOv10vehículo inteligenteConvolución flexible de varias escalasFusión auxiliar poco profundadetección del objetivo

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Área de la Ciencia:

  • Visión por computadora
  • Inteligencia artificial
  • Sistemas de transporte inteligentes

Sus antecedentes:

  • Los modelos de detección de objetos a menudo enfrentan desafíos con detecciones falsas y perdidas en escenarios complejos de conducción inteligente.
  • Los modelos existentes pueden tener altas cargas computacionales y grandes tamaños, lo que limita su aplicación en tiempo real.

Objetivo del estudio:

  • Optimizar el algoritmo YOLOv10 para mejorar la precisión de detección de objetos y reducir la complejidad del modelo en la conducción inteligente.
  • Desarrollar un marco de detección más eficiente y eficaz para los vehículos autónomos.

Principales métodos:

  • Convolución flexible multiscala diseñada (MSFC) para capturar información multiscala simultánea, reduciendo la profundidad de la red y el costo computacional.
  • Se reconstruyó la red de cuello utilizando Fusión Auxiliar Superficial (SAF) y Fusión Auxiliar Avanzada (AAF) para una mejor extracción de características a escala múltiple.
  • Mejoró la cabeza de detección con convolución de múltiples escalas y un mecanismo de atención adaptativa de canal para la extracción de características diversas y precisas.

Principales resultados:

  • El modelo optimizado YOLOv10 logró un tamaño de archivo de 13,4 MB, una reducción del 11,8% en comparación con el original.
  • El modelo alcanzó una precisión media (mAP@0.5) del 93,0%, lo que demuestra una precisión de detección superior.
  • El modelo mejorado superó a los modelos convencionales de detección de objetos en rendimiento general, equilibrando la precisión y el tamaño.

Conclusiones:

  • Las mejoras propuestas mejoran significativamente el rendimiento de YOLOv10 para aplicaciones de conducción inteligente.
  • Este modelo optimizado proporciona una solución práctica para la detección de objetos en tiempo real, equilibrando una alta precisión con recursos computacionales reducidos.
  • El marco desarrollado ofrece un sistema de detección robusto para escenarios de conducción inteligente.