Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

Chaves, Deisy; Saikia, Surajit; Fernández-Robles, Laura; Alegre, Enrique; Trujillo, María

doi:10.4995/riai.2018.10229

Cited by 13 publications

(11 citation statements)

References 72 publications

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“…Yang et al [62] highlighted the problems of variable scale and multi-orientation, designing a detection system based on the Inception [63] architecture and the deformable Position Sensitive RoI pooling (PSRoI) technique to detect curved text. In the case of multi-language text, special characters must be taken into account before deciding which object detection methods to use [64].…”

Section: Text Spotting With Deep Learningmentioning

confidence: 99%

A survey on methods, datasets and implementations for scene text spotting

Blanco‐Medina

Fidalgo

Alegre

et al. 2022

IET Image Processing

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Text Spotting is the union of the tasks of detection and transcription of the text that is present in images. Due to the various problems often found when retrieving text, such as orientation, aspect ratio, vertical text or multiple languages in the same image, this can be a challenging task. In this paper, the most recent methods and publications in this field are analysed and compared. Apart from presenting features already seen in other surveys, such as their architectures and performance on different datasets, novel perspectives for comparison are also included, such as the hardware, software, backbone architectures, main problems to solve, or programming languages of the algorithms. The review highlights information often omitted in other studies, providing a better understanding of the current state of research in Text Spotting, from 2016 to 2022, current problems and future trends, as well as establishing a baseline for future methods development, comparison of results and serving as guideline for choosing the most appropriate method to solve a particular problem.

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Section: Text Spotting With Deep Learningmentioning

confidence: 99%

A survey on methods, datasets and implementations for scene text spotting

Blanco‐Medina

Fidalgo

Alegre

et al. 2022

IET Image Processing

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“…A esto se añaden las limitaciones en la calidad de las imágenes recibidas, la diversidad de los objetos de interés y lo desestructurado de los entornos [3]. De ahí que se necesiten nuevas herramientas de inteligencia artificial para identificar información útil [9] [8].…”

Section: Introductionunclassified

Análisis de técnicas de aumento de datos y entrenamiento en YOLOv3 para detección de objetos en imágenes RGB y TIR del UMA-SAR Dataset

Vázquez-Martín¹,

Mandow²,

Morales-Rodriguez³

et al. 2021

Xlii Jornadas De Automática : Libro De Actas

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El uso de imágenes de los espectros visible (RGB) e infrarrojo térmico (TIR) para la detección de objetos puede resultar crucial en aplicaciones donde las condiciones de visibilidad están limitadas, como la robótica para búsqueda y rescate en catástrofes. Para ello resulta beneficioso analizar cómo las técnicas de aprendizaje profundo basadas en redes neuronales convolucionales (CNN) pueden aplicarse a ambas modalidades. En este artículo se analizan diferentes configuraciones y parámetros para el entrenamiento de CNN tanto para imágenes térmicas como para imágenes equivalentes del espectro visible. En concreto, se aborda el problema del sobre-entrenamiento para determinar una configuración eficaz de técnicas de aumento de datos y parada temprana. El caso de estudio se ha realizado con la red de código abierto YOLOv3, pre-entrenada con el dataset RGB COCO y optimizada (o re-entrenada) con el conjunto público de datos UMA-SAR dataset, que incluye pares de imágenes RGB y TIR obtenidas en ejercicios realistas de rescate.

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“…The availability of huge amounts of data, hardware resources and machine learning techniques allow to train computers to derive meaningful information from images. Thus, CSEM can be identified with image classification techniques [5], and its content can be explored using object detection methods [6][7][8][9] and object recognition techniques [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

Cited by 13 publications

References 72 publications

A survey on methods, datasets and implementations for scene text spotting

A survey on methods, datasets and implementations for scene text spotting

Análisis de técnicas de aumento de datos y entrenamiento en YOLOv3 para detección de objetos en imágenes RGB y TIR del UMA-SAR Dataset

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

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