PROTOTIPO FUNCIONAL PARA CLASIFICACIÓN DE IMÁGENES CON SALIDA DE AUDIO EN UN SISTEMA EMBEBIDO CON RED NEURONAL CONVOLUCIONAL (FUNCTIONAL PROTOTYPE FOR CLASSIFICATION OF IMAGES WITH AUDIO OUTPUT IN AN EMBEDDED SYSTEM USING CONVOLUTIONAL NEURAL NETWORK)

Fidel López Saca, Andrés Ferreyra Ramírez, Carlos Avilés Cruz

Resumen


En los últimos años, las redes neuronales convolucionales, han tenido una gran popularidad en aplicaciones de clasificación de imágenes, principalmente porque superan en rendimiento a los algoritmos tradicionales. Sin embargo, su alto costo computacional complica su implementación en sistemas embebidos con pocos recursos como las Raspberry Pi 3. Para superar este problema, se puede hacer uso del “Neural Compute Stick”, un dispositivo desarrollado recientemente, que integra una GPU en la que se puede cargar una red neuronal convolucional pre-entrenada. En este artículo se presenta un prototipo basado en la Raspberry Pi 3, que realiza clasificación de imágenes con reproducción de audio. La clasificación se realizada con la red GoogleNet, la cual es entrenada fuera de línea, implementada en un NCS e integrada a la tarjeta Raspberry Pi 3. En el sistema propuesto, la imagen que ingresa a través de una cámara web, es clasificada y etiquetada con la red convolucional y finalmente la etiqueta es traducida en audio por el sistema embebido para describir el objeto encontrado en la imagen.

In recent years, convolutional neural networks (CNN) have become very popular in image classification applications, mainly because they outperform traditional algorithms in performance. However, its high computational cost complicates its implementation in embedded systems with few resources such as Raspberry Pi 3. To overcome this problem, a "Neural Compute Stick" (NCS) can be used, which integrates a GPU. In the NCS can be loaded a pre-trained convolutional neural network. This article presents a prototype based on a Raspberry Pi 3, which performs image classification with audio reproduction. The classification is done through a GoogleNet net, which is trained offline, implemented in the NCS and integrated with the Raspberry Pi 3 card. In the proposed system, the image that enters through a webcam is classified and tagged with the CNN. Finally, the tag is translated into an audio file to be heard.


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