Search Results for author: Rayson Laroca

Found 26 papers, 12 papers with code

Leveraging Model Fusion for Improved License Plate Recognition

no code implementations8 Sep 2023 Rayson Laroca, Luiz A. Zanlorensi, Valter Estevam, Rodrigo Minetto, David Menotti

License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement.

License Plate Recognition Management

Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition

no code implementations10 Apr 2023 Rayson Laroca, Valter Estevam, Alceu S. Britto Jr., Rodrigo Minetto, David Menotti

This work draws attention to the large fraction of near-duplicates in the training and test sets of datasets widely adopted in License Plate Recognition (LPR) research.

License Plate Recognition

DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem

1 code implementation10 Mar 2023 Bruno A. Krinski, Daniel V. Ruiz, Rayson Laroca, Eduardo Todt

Our findings show that GAN-based techniques and spatial-level transformations are the most promising for improving the learning of deep models on this problem, with the StarGANv2 + F with a probability of 0. 3 achieving the highest F-score value on the Ricord1a dataset in the unified training strategy.

Computed Tomography (CT) Data Augmentation +1

Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution

2 code implementations30 Oct 2022 Valfride Nascimento, Rayson Laroca, Jorge de A. Lambert, William Robson Schwartz, David Menotti

The License Plate Recognition (LPR) field has made impressive advances in the last decade due to novel deep learning approaches combined with the increased availability of training data.

Image Super-Resolution License Plate Recognition +1

Global Semantic Descriptors for Zero-Shot Action Recognition

1 code implementation24 Sep 2022 Valter Estevam, Rayson Laroca, Helio Pedrini, David Menotti

This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences.

Action Classification Action Recognition +2

A First Look at Dataset Bias in License Plate Recognition

no code implementations23 Aug 2022 Rayson Laroca, Marcelo Santos, Valter Estevam, Eduardo Luz, David Menotti

We performed experiments on eight datasets, four collected in Brazil and four in mainland China, and observed that each dataset has a unique, identifiable "signature" since a lightweight classification model predicts the source dataset of a license plate (LP) image with more than 95% accuracy.

License Plate Recognition

Image-based Automatic Dial Meter Reading in Unconstrained Scenarios

1 code implementation8 Jan 2022 Gabriel Salomon, Rayson Laroca, David Menotti

The replacement of analog meters with smart meters is costly, laborious, and far from complete in developing countries.

Dial Meter Reading

On the Cross-dataset Generalization in License Plate Recognition

1 code implementation2 Jan 2022 Rayson Laroca, Everton V. Cardoso, Diego R. Lucio, Valter Estevam, David Menotti

Automatic License Plate Recognition (ALPR) systems have shown remarkable performance on license plates (LPs) from multiple regions due to advances in deep learning and the increasing availability of datasets.

Data Augmentation License Plate Detection +4

Dense Video Captioning Using Unsupervised Semantic Information

1 code implementation15 Dec 2021 Valter Estevam, Rayson Laroca, Helio Pedrini, David Menotti

We introduce a method to learn unsupervised semantic visual information based on the premise that complex events (e. g., minutes) can be decomposed into simpler events (e. g., a few seconds), and that these simple events are shared across several complex events.

Dense Video Captioning

A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios

no code implementations24 Nov 2020 Luiz A. Zanlorensi, Rayson Laroca, Diego R. Lucio, Lucas R. Santos, Alceu S. Britto Jr., David Menotti

Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region.

Face Recognition Image Classification +1

Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning

no code implementations30 Oct 2020 Rayson Laroca, Alessander Cidral Boslooper, David Menotti

In this work, we present a robust and efficient solution for counting and identifying train wagons using computer vision and deep learning.

Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines

no code implementations6 May 2020 Gabriel Salomon, Rayson Laroca, David Menotti

Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries.

Dial Meter Reading

Ocular Recognition Databases and Competitions: A Survey

no code implementations21 Nov 2019 Luiz A. Zanlorensi, Rayson Laroca, Eduardo Luz, Alceu S. Britto Jr., Luiz S. Oliveira, David Menotti

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information.

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

1 code implementation13 Nov 2019 Icaro O. de Oliveira, Rayson Laroca, David Menotti, Keiko V. O. Fonseca, Rodrigo Minetto

To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate.

Fine-Grained Vehicle Classification License Plate Detection +3

An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector

1 code implementation4 Sep 2019 Rayson Laroca, Luiz A. Zanlorensi, Gabriel R. Gonçalves, Eduardo Todt, William Robson Schwartz, David Menotti

This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules.

Data Augmentation License Plate Detection +2

Simultaneous Iris and Periocular Region Detection Using Coarse Annotations

no code implementations31 Jul 2019 Diego R. Lucio, Rayson Laroca, Luiz A. Zanlorensi, Gladston Moreira, David Menotti

In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN.

Iris Segmentation

Convolutional Neural Networks for Automatic Meter Reading

no code implementations25 Feb 2019 Rayson Laroca, Victor Barroso, Matheus A. Diniz, Gabriel R. Gonçalves, William Robson Schwartz, David Menotti

This dataset is, to the best of our knowledge, three times larger than the largest public dataset found in the literature and contains a well-defined evaluation protocol to assist the development and evaluation of AMR methods.

Counter Recognition Data Augmentation +2

Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks

no code implementations4 Sep 2018 Cides S. Bezerra, Rayson Laroca, Diego R. Lucio, Evair Severo, Lucas F. Oliveira, Alceu S. Britto Jr., David Menotti

In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described.

Iris Segmentation

Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation

no code implementations22 Jun 2018 Diego R. Lucio, Rayson Laroca, Evair Severo, Alceu S. Britto Jr., David Menotti

The initial and paramount step for performing this type of recognition is the segmentation of the region of interest, i. e. the sclera.

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

1 code implementation26 Feb 2018 Rayson Laroca, Evair Severo, Luiz A. Zanlorensi, Luiz S. Oliveira, Gabriel Resende Gonçalves, William Robson Schwartz, David Menotti

First, in the SSIG dataset, composed of 2, 000 frames from 101 vehicle videos, our system achieved a recognition rate of 93. 53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89. 80% and 93. 03%, respectively) and considerably outperforming previous results (81. 80%).

Data Augmentation License Plate Detection +2

Cannot find the paper you are looking for? You can Submit a new open access paper.