Traffic Sign Detection and Recognition Based on Convolutional Neural Network

As autonomous vehicles are developing and maturing the technology to implement the domestic autonomous vehicles. The critical technological problem for self-driving vehicles is traffic sign detection and recognition. A traffic sign recognition system is essential for an intelligent transportation system. The digital image processing techniques for object recognition and extraction of features from visual objects is a huge process and include many conversions and pre-processing steps. A deep learning-based convolutional neural network (CNN) model is one of the suitable approach for traffic sign detection and recognition. This model has overcome significant shortcomings of traditional visual object detection approaches. This paper proposed a traffic sign identification and detection system. The proposed design and strategy are implemented using the Tensorflow framework in google colab environment. The experiment is applied on the publicly available traffic sign data sets. The defined deep convolution neural network based model experimental results achieved 94.52% and 80.85% precision and recall respectively. Improving the seep of recognition and identifying appropriate features of traffic sign objects are addressed using deep learning-based encoders and transformers. &nbsp

Sistema de posicionamento robotizado segundo o conceito da indúsria 4.0

Trabalho final de mestrado para obtenção do grau de mestre em Engenharia MecânicaCom a evolução da automação industrial, existe a necessidade de automatizar até os processos mais simples, de forma a manter as empresas industriais competitivas. O objectivo deste Trabalho Final de Mestrado é automatizar o processo de posicionamento do sistema de lavagem interior dos tanques dos camiões-cisterna, com supervisão a partir da Internet. Neste trabalho de projecto desenvolveu-se um sistema de posicionamento robotizado com controlo de posição através da imagem de câmaras de vídeo. O algoritmo de visão foi programado no software Matlab com auxílio de bibliotecas de visão computacional e de comunicação com um controlador de motores de passo desenvolvido em arquitectura de Arduino. O desenvolvimento foi feito dentro do conceito da indústria 4.0, ou seja, todo o sistema é totalmente automático com excepção da introdução do número de bocas pelo operador. Foram testados controladores com comunicação à base do protocolo Ethernet, nomeadamente o PC com o cliente do Matlab a comunicar com um servidor Apache para que seja possível estabelecer a conexão entre o sistema e a supervisão na Internet. Todo o sistema pode ser supervisionado através de um smartphone ou tablet a partir do browser instalado. Construiu-se também um protótipo à escala, seleccionado de modo a validar o algoritmo de visão e as simulações efectuadas em Matlab. O protótipo é constituído por uma estrutura em perfis V-slot, motores de passo, o controlador e câmara USB, que embora tenha uma menor qualidade de imagem, foi suficiente para validar o algoritmo de visão implementado.With the evolution of industrial automation, there is a need to automate even the simplest processes, to keep industrial companies competitive. The objective of this Master’s Thesis is to automate the positioning system of an internal washing process of tanker trucks, with supervision from the Web. In this project work, a robot positioning system was developed with position control through the image of video cameras. The vision algorithm was programmed in Matlab software with the help of computer vision toolboxes and communication with a stepper motor controller developed in Arduino architecture. The development was done within the concept of the industry 4.0, that is, the whole system is totally automatic except for the introduction of the number of holes by the operator. Controllers were tested with communication based on the Ethernet protocol, namely the PC with the Matlab client to communicate with an Apache server so that it is possible to establish the connection between the system and the supervision on the web. The entire system can be supervised through a smartphone or tablet from the installed browser. A prototype was also built to scale, selected to validate the vision algorithm and the simulations performed in Matlab. The prototype consists of a structure in V-slot profiles, as well as the controller and USB camera that although having a lower quality of image, was enough to validate the implemented vision algorithm.N/