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Found 26 papers, 12 papers with code
Leveraging Model Fusion for Improved License Plate Recognition
License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement.
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
As a result, the generated images have a Structural Similarity Index Measure (SSIM) of less than 0. 10.
Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition
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.
DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem
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.
Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution
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.
Global Semantic Descriptors for Zero-Shot Action Recognition
This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences.
Face Super-Resolution Using Stochastic Differential Equations
To the best of our knowledge, this is the first time SDEs have been used for such an application.
A First Look at Dataset Bias in License Plate Recognition
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.
Image-based Automatic Dial Meter Reading in Unconstrained Scenarios
The replacement of analog meters with smart meters is costly, laborious, and far from complete in developing countries.
On the Cross-dataset Generalization in License Plate Recognition
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.
Tell me what you see: A zero-shot action recognition method based on natural language descriptions
To the best of our knowledge, this is the first work to represent both videos and labels with descriptive sentences.
Dense Video Captioning Using Unsupervised Semantic Information
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.
A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios
Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region.
Ranked #1 on
Image Classification
on Imbalanced CUB-200-2011
Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning
In this work, we present a robust and efficient solution for counting and identifying train wagons using computer vision and deep learning.
Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach
Existing approaches for image-based Automatic Meter Reading (AMR) have been evaluated on images captured in well-controlled scenarios.
Ranked #1 on
Meter Reading
on UFPR-AMR
Image-based Automatic Meter Reading
Optical Character Recognition (OCR)
Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines
Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries.
Ranked #1 on
Meter Reading
on UFPR-ADMR-v1
Ocular Recognition Databases and Competitions: A Survey
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
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
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.
Ranked #1 on
License Plate Recognition
on Caltech Cars
Simultaneous Iris and Periocular Region Detection Using Coarse Annotations
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.
Convolutional Neural Networks for Automatic Meter Reading
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.
Ranked #3 on
Meter Reading
on UFPR-AMR
Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described.
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments
The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness.
Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation
The initial and paramount step for performing this type of recognition is the segmentation of the region of interest, i. e. the sclera.
A Benchmark for Iris Location and a Deep Learning Detector Evaluation
The iris is considered as the biometric trait with the highest unique probability.
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
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%).
Ranked #2 on
License Plate Recognition
on SSIG-SegPlate
