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Detalhes

Detalhes

  • Nome

    Vitor Manuel Filipe
  • Cluster

    Informática
  • Cargo

    Investigador Coordenador
  • Desde

    01 outubro 2012
002
Publicações

2021

An Intelligent Predictive Maintenance Approach Based on End-of-Line Test Logfiles in the Automotive Industry

Autores
Vicêncio, D; Silva, H; Soares, S; Filipe, V; Valente, A;

Publicação
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Industrial IoT Technologies and Applications

Abstract

2021

Classification of car parts using deep neural network

Autores
Khanal, SR; Amorim, EV; Filipe, V;

Publicação
Lecture Notes in Electrical Engineering

Abstract
Quality automobile inspection is one of the critical application areas to achieve better quality at low cost and can be obtained with the advance computer vision technology. Whether for the quality inspection or the automatic assembly of automobile parts, automatic recognition of automobile parts plays an important role. In this article, vehicle parts are classified using deep neural network architecture designed based on ConvNet. The public dataset available in CompCars [1] were used to train and test a VGG16 deep learning architecture with a fully connected output layer of 8 neurons. The dataset has 20,439 RGB images of eight interior and exterior car parts taken from the front view. The dataset was first separated for training and testing purpose, and again training dataset was divided into training and validation purpose. The average accuracy of 93.75% and highest accuracy of 97.2% of individual parts recognition were obtained. The classification of car parts contributes to various applications, including car manufacturing, model verification, car inspection system, among others. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

2021

Engine labels detection for vehicle quality verification in the assembly line: A machine vision approach

Autores
Capela, S; Silva, R; Khanal, SR; Campaniço, AT; Barroso, J; Filipe, V;

Publicação
Lecture Notes in Electrical Engineering

Abstract
The automotive industry has an extremely high-quality product standard, not just for the security risks each faulty component can present, but the very brand image it must uphold at all times to stay competitive. In this paper, a prototype model is proposed for smart quality inspection using machine vision. The engine labels are detected using Faster-RCNN and YOLOv3 object detection algorithms. All the experiments were carried out using a custom dataset collected at an automotive assembly plant. Eight engine labels of two brands (Citroën and Peugeot) and more than ten models were detected. The results were evaluated using the metrics Intersection of Union (IoU), mean of Average Precision (mAP), Confusion Matrix, Precision and Recall. The results were validated in three folds. The models were trained using a custom dataset containing images and annotation files collected and prepared manually. Data Augmentation techniques were applied to increase the image diversity. The result without data augmentation was 92.5%, and with it the value was up-to 100%. Faster-RCNN has more accurate results compared to YOLOv3. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

2021

Worker Support and Training Tools to Aid in Vehicle Quality Inspection for the Automotive Industry

Autores
Campaniço, AT; Khanal, S; Paredes, H; Filipe, V;

Publicação
Communications in Computer and Information Science - Technology and Innovation in Learning, Teaching and Education

Abstract

2021

Low-Cost and Reduced-Size 3D-Cameras Metrological Evaluation Applied to Industrial Robotic Welding Operations

Autores
de Souza, JPC; Rocha, LF; Filipe, VM; Boaventura Cunha, J; Moreira, AP;

Publicação
2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Abstract

Teses
supervisionadas

2020

Facial image processing to monitor physical exercise intensity

Autor
Salik Ram Khanal

Instituição
UTAD

2020

Não robot applied to the development of cognitive skills

Autor
Ana Maria da Cruz Freire

Instituição
UP-FEUP

2020

MODELO DE FATORES INFLUENCIADORES DA ADOÇÃO DE CRM EM MUNICÍPIOS

Autor
Jorge Manuel Pereira Duque

Instituição
UTAD

2020

Player market cap: aplicação móvel para negociação de ativos digitais usando tecnologia blockchain

Autor
Nélson Miguel Alves Gomes

Instituição
UP-FEUP

2019

Facial image processing to monitor physical exercise intensity

Autor
Salik Ram Khanal

Instituição
UTAD