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Sobre

Armando Sousa received his Ph.D. degrees in the area of Robotics at the University of Porto, Portugal in 2004.
He is currently an Auxiliary Professor in the same faculty and an integrated researcher in the INESCTEC (Institute for Systems and Computer Engineering of Porto - Technology and Science).
He received several international awards in robotic soccer under the RoboCup Federation (mainly in the small size league). He has also received the Pedagogical Excellence award of the UP in year 2015.
His main research interests include education, robotics, data fusion and vision systems. He has co-authored over 50 international peer-reviewed publications and participated in over 10 international projects in the areas of education and robotics.

Tópicos
de interesse
Detalhes

Detalhes

003
Publicações

2021

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

Autores
de Aguiar, ASP; dos Santos, FN; Placido Sobreira, HM; Cunha, JB; de Sousa, AJM;

Publicação
Robotics and Autonomous Systems

Abstract

2021

Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection

Autores
Aguiar, AS; Monteiro, NN; dos Santos, FN; Pires, EJS; Silva, D; Sousa, AJ; Boaventura Cunha, J;

Publicação
Agriculture

Abstract
The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified.

2021

Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification

Autores
Fitas, R; Rocha, B; Costa, V; Sousa, A;

Publicação
Journal of Imaging

Abstract
Cork stoppers were shown to have unique characteristics that allow their use for authentication purposes in an anti-counterfeiting effort. This authentication process relies on the comparison between a user’s cork image and all registered cork images in the database of genuine items. With the growth of the database, this one-to-many comparison method becomes lengthier and therefore usefulness decreases. To tackle this problem, the present work designs and compares hashing-assisted image matching methods that can be used in cork stopper authentication. The analyzed approaches are the discrete cosine transform, wavelet transform, Radon transform, and other methods such as difference hash and average hash. The most successful approach uses a 1024-bit hash length and difference hash method providing a 98% accuracy rate. By transforming the image matching into a hash matching problem, the approach presented becomes almost 40 times faster when compared to the literature.

2021

Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Autores
da Silva, DQ; Aguiar, AS; Dos Santos, FN; Sousa, AJ; Rabino, D; Biddoccu, M; Bagagiolo, G; Delmastro, M;

Publicação
Agriculture

Abstract
Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

2021

A Comparative Analysis for 2D Object Recognition: A Case Study with Tactode Puzzle-Like Tiles

Autores
Silva, D; Sousa, A; Costa, V;

Publicação
Journal of Imaging

Abstract
Object recognition represents the ability of a system to identify objects, humans or animals in images. Within this domain, this work presents a comparative analysis among different classification methods aiming at Tactode tile recognition. The covered methods include: (i) machine learning with HOG and SVM; (ii) deep learning with CNNs such as VGG16, VGG19, ResNet152, MobileNetV2, SSD and YOLOv4; (iii) matching of handcrafted features with SIFT, SURF, BRISK and ORB; and (iv) template matching. A dataset was created to train learning-based methods (i and ii), and with respect to the other methods (iii and iv), a template dataset was used. To evaluate the performance of the recognition methods, two test datasets were built: tactode_small and tactode_big, which consisted of 288 and 12,000 images, holding 2784 and 96,000 regions of interest for classification, respectively. SSD and YOLOv4 were the worst methods for their domain, whereas ResNet152 and MobileNetV2 showed that they were strong recognition methods. SURF, ORB and BRISK demonstrated great recognition performance, while SIFT was the worst of this type of method. The methods based on template matching attained reasonable recognition results, falling behind most other methods. The top three methods of this study were: VGG16 with an accuracy of 99.96% and 99.95% for tactode_small and tactode_big, respectively; VGG19 with an accuracy of 99.96% and 99.68% for the same datasets; and HOG and SVM, which reached an accuracy of 99.93% for tactode_small and 99.86% for tactode_big, while at the same time presenting average execution times of 0.323 s and 0.232 s on the respective datasets, being the fastest method overall. This work demonstrated that VGG16 was the best choice for this case study, since it minimised the misclassifications for both test datasets.

Teses
supervisionadas

2020

Robotic Bin Picking of Entangled Tubes

Autor
Gonçalo da Mota Laranjeira Torres Leão

Instituição
UP-FEUP

2020

Simulating and upgrading PiGaming Mixed Reality setup with Artificial Intelligence

Autor
José Nuno Amaro Freixo

Instituição
UP-FEUP

2020

Radio-Frequency Based Location Service

Autor
João Filipe Lopes de Carvalho

Instituição
UP-FEUP

2020

Segmentation and detection of Woody Trunks using Deep Learning for Agricultural Robotics

Autor
Nuno Gonçalo Pinto Machado Namora Monteiro

Instituição
UP-FEUP

2020

Tools and Control Experiences using TCLab Arduino Kit

Autor
Bruno Fernando Marques Aguiar

Instituição
UP-FEUP