Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Vitor Manuel Filipe
  • Cargo

    Investigador Coordenador
  • Desde

    01 outubro 2012
005
Publicações

2025

Automated optical system for quality inspection on reflective parts

Autores
Nascimento, R; Rocha, CD; Gonzalez, DG; Silva, T; Moreira, R; Silva, MF; Filipe, V; Rocha, LF;

Publicação
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Abstract
The growing demand for high-quality components in various industries, particularly in the automotive sector, requires advanced and reliable inspection methods to maintain competitive standards and support innovation. Manual quality inspection tasks are often inefficient and prone to errors due to their repetitive nature and subjectivity, which can lead to attention lapses and operator fatigue. The inspection of reflective aluminum parts presents additional challenges, as uncontrolled reflections and glare can obscure defects and reduce the reliability of conventional vision-based methods. Addressing these challenges requires optimized illumination strategies and robust image processing techniques to enhance defect visibility. This work presents the development of an automated optical inspection system for reflective parts, focusing on components made of high-pressure diecast aluminum used in the automotive industry. The reflective nature of these parts introduces challenges for defect detection, requiring optimized illumination and imaging methods. The system applies deep learning algorithms and uses dome light to achieve uniform illumination, enabling the detection of small defects on reflective surfaces. A collaborative robotic manipulator equipped with a gripper handles the parts during inspection, ensuring precise positioning and repeatability, which improves both the efficiency and effectiveness of the inspection process. A flow execution-based software platform integrates all system components, enabling seamless operation. The system was evaluated with Schmidt Light Metal Group using three custom datasets to detect surface porosities and inner wall defects post-machining. For surface porosity detection, YOLOv8-Mosaic, trained with cropped images to reduce background noise, achieved a recall value of 84.71% and was selected for implementation. Additionally, an endoscopic camera was used in a preliminary study to detect defects within the inner walls of holes. The industrial trials produced promising results, demonstrating the feasibility of implementing a vision-based automated inspection system in various industries. The system improves inspection accuracy and efficiency while reducing material waste and operator fatigue.

2025

Towards Non-invasive Detection of Gastric Intestinal Metaplasia: A Deep Learning Approach Using Narrow Band Imaging Endoscopy

Autores
Capela, S; Lage, J; Filipe, V;

Publicação
Lecture Notes in Networks and Systems

Abstract
Gastric cancer, ranking as the sixth most prevalent cancer globally and a leading cause of cancer-related mortality, follows a sequential progression known as Correa's cascade, spanning from chronic gastritis to eventual malignancy. Although endoscopy exams using Narrow Band Imaging are recommended by internationally accepted guidelines for diagnostic Gastric Intestinal Metaplasia, the lack of endoscopists with the skill to assess the NBI image patterns and the disagreement between endoscopists when assessing the same image, have made the use of biopsies the gold standard still used today. This proposal doctoral thesis seeks to address the challenge of developing a Computer-Aided Diagnosis solution for GIM detection in NBI endoscopy exams, aligning with the established guidelines, the Management of Epithelial Precancerous Conditions and Lesions in the Stomach. Our approach will involve a dataset creation that follows the standardized approach for histopathological classification of gastrointestinal biopsies, the Sydney System recommended by MAPS II guidelines, and annotation by gastroenterology experts. Deep learning models, including Convolutional Neural Networks, will be trained and evaluated, aiming to establish an internationally accepted AI-driven alternative to biopsies for GIM detection, promising expedited diagnosis, and cost reduction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

2025

Riding with Intelligence: Advanced Rider Assistance Systems Proposal

Autores
Silva, J; Ullah, Z; Reis, A; Pires, E; Pendão, C; Filipe, V;

Publicação
Lecture Notes in Networks and Systems - Distributed Computing and Artificial Intelligence, Special Sessions I, 21st International Conference

Abstract

2024

Automated Detection of Refilling Stations in Industry Using Unsupervised Learning

Autores
Ribeiro J.; Pinheiro R.; Soares S.; Valente A.; Amorim V.; Filipe V.;

Publicação
Lecture Notes in Mechanical Engineering

Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations’ efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations.

2024

An Overview of Explainable Artificial Intelligence in the Industry 4.0 Context

Autores
Teixeira P.; Amorim E.V.; Nagel J.; Filipe V.;

Publicação
Lecture Notes in Mechanical Engineering

Abstract
Artificial intelligence (AI) has gained significant evolution in recent years that, if properly harnessed, may meet or exceed expectations in a wide range of application fields. However, because Machine Learning (ML) models have a black-box structure, end users frequently seek explanations for the predictions made by these learning models. Through tools, approaches, and algorithms, Explainable Artificial Intelligence (XAI) gives descriptions of black-box models to better understand the models’ behaviour and underlying decision-making mechanisms. The AI development in companies enables them to participate in Industry 4.0. The need to inform users of transparent algorithms has given rise to the research field of XAI. This paper provides a brief overview and introduction to the subject of XAI while highlighting why this topic is generating more and more attention in many sectors, such as industry.

Teses
supervisionadas

2023

Computer vision in industrial processes: A case study at continental advanced antenna

Autor
José Pedro Matos Ribeiro

Instituição
UTAD

2023

Desenvolvimento de microsserviços .Net para serviços de mobilidade

Autor
Filipe Manuel da Silva Valadares

Instituição
UTAD

2023

O impacto do desafio percecionado na imersão narrativa dos videojogos

Autor
José Miguel Vieira Domingues

Instituição
UTAD

2023

ForestMP: Multimodal perception system for robotics in forestry applications

Autor
Daniel Queirós da Silva

Instituição
UTAD

2023

Estratégias e Modelos para Estimular o Engagement de Estudantes no Ensino Superior

Autor
Viktoriya Limonova

Instituição
UTAD