Publications

Gustavo de Moura
Master's Degree 2024

A. Menezes, Gustavo de Moura, C. Alves, André C. P. L. F. de Carvalho.
Neural Networks - May 2022

This systematic review paper explores the field of Continual Learning, primarily focusing on class-incremental object detection. In this review, we analyze the current strategies proposed to tackle the problem of class-incremental object detection. We provide a short and systematic review of the methods, an evaluation of the existing approaches, and an overview of the current trends and possible future research directions.

Available on arXiv.
Published on the Neural Networks Journal.

Gustavo de Moura Souza, Claudio F. M. Toledo.
WCCI CEC - July 2020

The present paper introduces a hybrid genetic algorithm for path planning problem with obstacle avoidance. The genetic algorithm is combined with Ray Casting (RC) algorithm, where RC is responsible to avoid obstacles and to find safe regions for emergency landing. Thus, the path planning system must deal with a non-convex environment when planning and re-planning trajectories. The system must also work embedded on the UAV running under a Raspberry Pi board. The hybrid method is evaluated over 50 benchmark maps from literature with satisfactory results reported.

Presented on the WCCI Congress on Evolutionary Computation (CEC) 2020.

V. Vannini, Gustavo de Moura, Claudio F.  M.  Toledo.
Smart Agricultural Technology - August 2023

This systematic review paper explores the field of Continual Learning, primarily focusing on class-incremental object detection. In this review, we analyze the current strategies proposed to tackle the problem of class-incremental object detection. We provide a short and systematic review of the methods, an evaluation of the existing approaches, and an overview of the current trends and possible future research directions.

Published on the Smart Agricultural Technology Journal.

A. Peterlevitz, Gustavo de Moura, C. Alves, et al.
IEEE Access - October 2023

In this work, we analyze the generation of synthetic data to account for a data-scarce real-world scenario, which includes aerial imagery and object detection of transmission towers and their components. We evaluate the impact of image-to-image translation methods as domain adaptation techniques. In this analysis, we explore training strategies to mitigate the domain shift between synthetic and real data.

Published on IEEE Access.

M. Carvalho*, A. Alcântara*, Gustavo de Moura Souza*, K. Bogdan*, M. Martins, E. Gadbem, L. Maia.
Rio Oil & Gas - December 2020

In this work, we present an approach based on Optical Character Recognition (OCR) and text processing techniques to automatically extract information from digital well drilling schematics reports to a structured template. We present results that indicates this work can be a starting point for the extraction of structured information from unstructured/semi-structured reports in the O&G field.

Presented on the Rio Oil & Gas 2020 conference.

*equal contribution

K. Bogdan, G. Megeto, R. Leal, Gustavo Souza, A. Valente, L. Kirsten .
ICDAR - September 2021

In this work, we propose a lightweight neural network to enhance photographed document images using a feature extraction that aggregates multi-scale features and a pixel-wise adjustment refinement step. We focused on aesthetics aspects of the images. Our method was able to lessen the negative effects of different artifacts, such as shadows and insufficient illumination, while also maintaining a good color consistency, resulting in a better final enhanced image.

Presented on ICDAR 2021 conference.