scholarly journals SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4587 ◽  
Author(s):  
Ángel Morera ◽  
Ángel Sánchez ◽  
A. Belén Moreno ◽  
Ángel D. Sappa ◽  
José F. Vélez
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
The Other ◽  
Single Shot ◽  
Partial Occlusion ◽  
True Positive ◽  
Detection Problem ◽  
Different Types ◽  
Outdoor Advertisement

This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.

scholarly journals Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery

2021 ◽  
Author(s):  
Lucas Prado Osco ◽  
Keiller Nogueira ◽  
Ana Paula Marques Ramos ◽  
Mayara Maezano Faita Pinheiro ◽  
Danielle Elis Garcia Furuya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
Citrus Orchard

Semantic Segmentation of Mammograms Using Pre-Trained Deep Neural Networks

2021 ◽  
Author(s):  
Rodrigo Leite Prates ◽  
Wilfrido Gomez-Flores ◽  
Wagner Pereira
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation

Shallow and Deep Artificial Neural Networks for Structural Reliability Analysis

2020 ◽  
Author(s):  
Wellison J. S. Gomes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Deep Neural Networks ◽  
Surrogate Models ◽  
The Other ◽  
Computational Costs ◽  
Structural Reliability Analysis ◽  
Artificial Neural

Abstract Surrogate models are efficient tools which have been successfully applied in structural reliability analysis, as an attempt to keep the computational costs acceptable. Among the surrogate models available in the literature, Artificial Neural Networks (ANNs) have been attracting research interest for many years. However, the ANNs used in structural reliability analysis are usually the shallow ones, based on an architecture consisting of neurons organized in three layers, the so-called input, hidden and output layers. On the other hand, with the advent of deep learning, ANNs with one input, one output, and several hidden layers, known as deep neural networks, have been increasingly applied in engineering and other areas. Considering that many recent publications have shown advantages of deep over shallow ANNs, the present paper aims at comparing these types of neural networks in the context of structural reliability. By applying shallow and deep ANNs in the solution of four benchmark structural reliability problems from the literature, employing Monte Carlo simulation and adaptive experimental designs, it is shown that, although good results are obtained for both types of ANNs, deep ANNs usually outperform the shallow ones.

scholarly journals Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks

2021 ◽  
Vol 3 (2) ◽  
pp. 61-79
Author(s):  
S Thivaharan ◽  
G Srivatsun
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Prediction Accuracy ◽  
Deep Neural Networks ◽  
Modern Technology ◽  
The Other ◽  
Data Generation ◽  
Class Prediction ◽  
The Social ◽  
Communication Devices

The amount of data generated by modern communication devices is enormous, reaching petabytes. The rate of data generation is also increasing at an unprecedented rate. Though modern technology supports storage in massive amounts, the industry is reluctant in retaining the data, which includes the following characteristics: redundancy in data, unformatted records with outdated information, data that misleads the prediction and data with no impact on the class prediction. Out of all of this data, social media plays a significant role in data generation. As compared to other data generators, the ratio at which the social media generates the data is comparatively higher. Industry and governments are both worried about the circulation of mischievous or malcontents, as they are extremely susceptible and are used by criminals. So it is high time to develop a model to classify the social media contents as fair and unfair. The developed model should have higher accuracy in predicting the class of contents. In this article, tensor flow based deep neural networks are deployed with a fixed Epoch count of 15, in order to attain 25% more accuracy over the other existing models. Activation methods like “Relu” and “Sigmoid”, which are specific for Tensor flow platforms support to attain the improved prediction accuracy.

scholarly journals Image Compression Based on Deep Learning: A Review

2021 ◽  
pp. 62-76
Author(s):  
Hajar Maseeh Yasin ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Compression ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Review Paper ◽  
High Accuracy ◽  
Machine Learning Methods ◽  
Digital Era ◽  
Different Types

Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.

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