A lightweight convolutional neural network for pose estimation of a planar model

Ocegueda-Hernández, Vladimir; Román-Godínez, Israel; Mendizabal-Ruiz, Gerardo

doi:10.1007/s00138-022-01292-z

Cited by 4 publications

(3 citation statements)

References 53 publications

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“…The prediction outcomes are analyzed using the metrics of accuracy, precision, recall, and F1-score [43][44][45][46]. The accuracy indicator displays the rate of model prediction accuracy across all parameters.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…The authors proposed a novel ground vibration monitoring strategy for MEMS-sensed data using a deep learning approach [44]. The following study created a network for magnitude estimation using convolutional and recurrent layers [45]. In subsequent research, ConvNetQuake was developed to identify nearby micro-earthquakes based on signal waveforms.…”

Section: B Deep Learning In Earthquake Forecastingmentioning

confidence: 99%

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2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Turarbek¹,

Adetbekov²,

Bektemesov³

2023

IJACSA

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Machine learning has advanced rapidly in the last decade, promising to significantly change and improve the function of big data analysis in a variety of fields. When compared to traditional methods, machine learning provides significant advantages in complex problem solving, computing performance, uncertainty propagation and handling, and decision support. In this paper, we present a novel end-to-end strategy for improving the overall accuracy of earthquake detection by simultaneously improving each step of the detection pipeline. In addition, we propose a Conv2D convolutional neural network (CNN) architecture for processing seismic waveforms collected across a geophysical system. The proposed Conv2D method for earthquake detection was compared to various machine-learning approaches and state-of-the-art methods. All of the methods used were trained and tested on real data collected in Kazakhstan over the last 97 years, from 1906 to 2022. The proposed model outperformed the other models with accuracy, precision, recall, and f-score scores of 63%, 82.4%, 62.7%, and 83%, respectively. Based on the results, it is possible to conclude that the proposed Conv2D model is useful for predicting realworld earthquakes in seismic zones.

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Section: Evaluation Methodsmentioning

confidence: 99%

Section: B Deep Learning In Earthquake Forecastingmentioning

confidence: 99%

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Turarbek¹,

Adetbekov²,

Bektemesov³

2023

IJACSA

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show abstract

“…Later, Merzić et al [39] used definition (18) in an algorithm for visual localization and found it valid and efficient to use, whereas Brégier et al [40] found reasonable the proposed u choice, and Chen et al [41] used it to build their own metric definition. Then, Kendall et al [42] and, recently, Ocegueda-Hernández et al [43] used definition (18) by choosing two different metrics for SO (3) and found it efficient to compute the pose error when estimating the position and orientation of a three-dimensional object from its projection onto a two-dimensional image. It is worth noting that the above-proposed direct deduction of the u value immediately solves the problem of choosing a suitable scaling factor together with the limitations highlighted by Rico-Martinez and Duffy [3] for finite rigid bodies.…”

Section: Applicative Examplementioning

confidence: 99%

Metrics proposed for measuring the distance between two rigid-body poses: review, comparison, and combination

Di Gregorio

2023

Robotica

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The concept of distance between two rigid-body poses is important in path planning, positioning precision, mechanism synthesis, and in many other applications. In the definition of such a distance, two approaches mainly prevail, which lead to a number of formulas devised to match the needs of motion tasks. Despite the different approaches and formulas, some important theoretical results, which drive toward distance-metrics definitions useful for design and application purposes, have been stated. This paper summarizes the two different approaches together with a critical review of the literature on the distance metrics they generated, and, then, it illustrates a technique, previously proposed by the author, for combining different metrics to obtain novel distance-metric definitions that are tailored to specific applications.

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A Pig Pose Estimation Model for Measuring Pig’s Body Size

Yang¹,

Qin²,

Sun³

et al. 2022

Lecture Notes in Computer Science

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A lightweight convolutional neural network for pose estimation of a planar model

Cited by 4 publications

References 53 publications

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Metrics proposed for measuring the distance between two rigid-body poses: review, comparison, and combination

A Pig Pose Estimation Model for Measuring Pig’s Body Size

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