franzmgarcia

Algoritmo-Planificacion

Algoritmo para resolver problemas de planificación de ruta

Chatgpt-News-Finder

franzmgarcia.github.io

Proyecto-Contador-T-A---TOF

Proyecto-contador-T-P

Proyecto-piano-Virtual

Proyecto-Robot-movil

ProyectoROs

React_Native_

React_Native_project

franzmgarcia/Algoritmo-Planificacion

Algoritmo para resolver problemas de planificación de ruta

franzmgarcia/Chatgpt-News-Finder

franzmgarcia/franzmgarcia.github.io

franzmgarcia/Proyecto-Contador-T-A---TOF

franzmgarcia/Proyecto-contador-T-P

franzmgarcia/Proyecto-piano-Virtual

franzmgarcia/Proyecto-Robot-movil

franzmgarcia/ProyectoROs

franzmgarcia/React_Native_

franzmgarcia/React_Native_project

franzmgarcia/Robot_webots

Simulación de movimiento de articulación de robot mediante el uso de webots

franzmgarcia/Semantic-segmentation-of-obstacles-lanes-and-rails-using-neural-networks-and-applications

Autonomous vehicles require strong detection systems to be safe for humans. This systems must to detect all the elements existing in the road systems, to have the ability of response in any situation. A good solution for this purpose, is the use of semantic segmentation of the elements in the road, using neural networks. In this work, I used the ERFNet network to do the semantic segmentation in real time, with an adaptation of the database BBD100K, modifying the labels to create new elements to be recognize. The training of the neural network provides an accuracy of 64.75%. Using the segmentation, I realized an algortihm to count the number of the roads existing in the road system, and the position of the vehicle in this system. This work, was realize in the Laboratory of Intelligent Systems, in Leganés, Spain.

franzmgarcia/Siamese-Neural-Network

Siamese Neural Network for global localization

franzmgarcia/test

franzmgarcia/test_2