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Detalhes

Detalhes

  • Nome

    André Dias
  • Cargo

    Investigador Sénior
  • Desde

    01 outubro 2011
  • Nacionalidade

    Portugal
  • Contactos

    +351228340554
    andre.dias@inesctec.pt
017
Publicações

2023

Automatic Detection of Corrosion in Large-Scale Industrial Buildings Based on Artificial Intelligence and Unmanned Aerial Vehicles

Autores
Lemos, R; Cabral, R; Ribeiro, D; Santos, R; Alves, V; Dias, A;

Publicação
APPLIED SCIENCES-BASEL

Abstract
In recent years, Artificial Intelligence (AI) provided essential tools to enhance the productivity of activities related to civil engineering, particularly in design, construction, and maintenance. In this framework, the present work proposes a novel AI computer vision methodology for automatically identifying the corrosion phenomenon on roofing systems of large-scale industrial buildings. The proposed method can be incorporated into computational packages for easier integration by the industry to enhance the inspection activities' performance. For this purpose, a dedicated image database with more than 8k high-resolution aerial images was developed for supervised training. An Unmanned Aerial Vehicle (UAV) was used to acquire remote georeferenced images safely and efficiently. The corrosion anomalies were manually annotated using a segmentation strategy summing up 18,381 instances. These anomalies were identified through instance segmentation using the Mask based Region-Convolution Neural Network (Mask R-CNN) framework adjusted to the created dataset. Some adjustments were performed to enhance the performance of the classification model, particularly defining an adequate input image size, data augmentation strategy, Intersection over a Union (IoU) threshold during training, and type of backbone network. The inferences show promising results, with correct detections even under complex backgrounds, poor illumination conditions, and instances of significantly reduced dimensions. Furthermore, in scenarios without a roofing system, the model proved reliable, not producing any false positive occurrences. The best model achieved metrics' values equal to 65.1% for the bounding box detection Average Precision (AP) and 59.2% for the mask AP, considering an IoU of 50%. Regarding classification metrics, the precision and recall were equal to 85.8% and 84.0%, respectively. The developed methodology proved to be extremely valuable for guiding infrastructure managers in taking physically informed decisions based on the real assets condition.

2023

GeoTec: A System for 3D Reconstruction in Underground Environment (Aveleiras Mine, Monastery of Tibães, NW Portugal)

Autores
Pires, A; Dias, A; Rodrigues, P; Silva, P; Santos, T; Oliveira, A; Ferreira, A; Almeida, J; Martins, A; Chaminé, I; Silva, E;

Publicação
Advances in Science, Technology and Innovation

Abstract

2023

Methodological insights from unmanned system technologies in a rock quarry environment and geomining heritage site: coupling LiDAR-based mapping and GIS geovisualisation techniques

Autores
Pires, A; Dias, A; Silva, P; Ferreira, A; Rodrigues, P; Santos, T; Oliveira, A; Freitas, L; Martins, A; Almeida, J; Silva, E; Chaminé, HI;

Publicação
Arabian Journal of Geosciences

Abstract

2022

Unmanned Aerial Vehicle for Wind-Turbine Inspection. Next Step: Offshore

Autores
Dias, A; Almeida, J; Oliveira, A; Santos, T; Martins, A; Silva, E;

Publicação
2022 OCEANS HAMPTON ROADS

Abstract
Offshore wind turbine application has been widespread in the last years, with an estimation that in 2030 will reach a total capacity of 234GW. Offshore wind farms introduce advantages in terms of environmental impact (noise, impact on birds, disrupted landscapes) and energy production (34% onshore and 43% offshore). Still, they also introduce scientific challenges in developing methodologies that allow wind farm inspection (preventive maintenance) safety for humans. This paper presents a UAV approach for autonomous inspection of inland windturbine and describes the field tests in Penela, Portugal. From the state-of-the-art available wind turbine inspection, in 2015, we carried out the first autonomous inspection with a UAV. The inspection of wind blades offshore is an ongoing project; therefore, the paper also presents the preliminary results with a simulation environment to validate the 3D LiDAR and the inspection procedure with new challenges effects: floating platform, wind gusts, and unknown initial blade position.

2021

Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

Autores
Loureiro, G; Dias, A; Martins, A; Almeida, J;

Publicação
REMOTE SENSING

Abstract
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area's roughness, and the spot's slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

Teses
supervisionadas

2022

Exploração de metodologias de Odometria e Mapeamento 3D baseado em LiDAR

Autor
EDUARDO DANIEL MACHADO MARQUES

Instituição
IPP-ISEP

2021

Sistema de Visão Computacional Low-Cost para Deteção e Contagem de Pessoas e Veículos em Smart Cities

Autor
MIGUEL MENDES AMADO

Instituição
IPP-ISEP

2021

Sistema de Visão Termográfico para Veículos Autónomos Aéreos

Autor
CARLOS MANUEL DE SOUSA FERRÁS

Instituição
IPP-ISEP

2021

Benchmark de Sistemas Embebidos para Machine Learning em Visão Computacional

Autor
MIGUEL ÂNGELO LOURENÇO LOPES

Instituição
IPP-ISEP

2021

Graph-SLAM Approach for Indoor UAV Localization in Warehouse Logistics Applications

Autor
JOSÉ FILIPE DA SILVA OLIVEIRA ANTUNES

Instituição
IPP-ISEP