scholarly journals Model Architecture of CNN for Recognition the Pandava Mask

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 99
Author(s):  
Andi Sanjaya ◽  
Endang Setyati ◽  
Herman Budianto
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Convolutional Neural Networks ◽  
Research Result ◽  
Trial Result ◽  
Trial Data ◽  
The Other ◽  
Architectural Model ◽  
Input Layer

This research was conducted to observe the use of architectural model Convolutional Neural Networks (CNN) LeNEt, which was suitable to use for Pandava mask objects. The Data processing in the research was 200 data for each class or similar with 1000 trial data. Architectural model CNN LeNET used input layer 32x32, 64x64, 128x128, 224x224 and 256x256. The trial result with the input layer 32x32 succeeded, showing a faster time compared to the other layer. The result of accuracy value and validation was not under fitted or overfit. However, when the activation of the second dense process as changed from the relu to sigmoid, the result was better in sigmoid, in the tem of time, and the possibility of overfitting was less. The research result had a mean accuracy value of 0.96.

scholarly journals Separable convolutional neural networks for facial expressions recognition

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Andry Chowanda
Keyword(s):  
Neural Networks ◽  
Social Interactions ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
The Other ◽  
Computational Power ◽  
Facial Cues ◽  
Emotional Recognition ◽  
Testing Accuracy ◽  
Facial Expressions Recognition

AbstractSocial interactions are important for us, humans, as social creatures. Emotions play an important part in social interactions. They usually express meanings along with the spoken utterances to the interlocutors. Automatic facial expressions recognition is one technique to automatically capture, recognise, and understand emotions from the interlocutor. Many techniques proposed to increase the accuracy of emotions recognition from facial cues. Architecture such as convolutional neural networks demonstrates promising results for emotions recognition. However, most of the current models of convolutional neural networks require an enormous computational power to train and process emotional recognition. This research aims to build compact networks with depthwise separable layers while also maintaining performance. Three datasets and three other similar architectures were used to be compared with the proposed architecture. The results show that the proposed architecture performed the best among the other architectures. It achieved up to 13% better accuracy and 6–71% smaller and more compact than the other architectures. The best testing accuracy achieved by the architecture was 99.4%.

scholarly journals INFERRING THE SCALE AND CONTENT OF A MAP USING DEEP LEARNING

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 17-24
Author(s):  
G. Touya ◽  
F. Brisebard ◽  
F. Quinton ◽  
A. Courtial
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Visually Impaired ◽  
The Other ◽  
Visually Impaired People ◽  
Impaired People ◽  
Tactile Maps

Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).

Complementary Object Tracking Using Average Peak-to-Correlation Energy

2021 ◽  
Author(s):  
Kosuke Honda ◽  
Hamido Fujita
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Correlation Energy ◽  
Target Object ◽  
The Other ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Evaluation Index ◽  
Siamese Network

In recent years, template-based methods such as Siamese network trackers and Correlation Filter (CF) based trackers have achieved state-of-the-art performance in several benchmarks. Recent Siamese network trackers use deep features extracted from convolutional neural networks to locate the target. However, the tracking performance of these trackers decreases when there are similar distractors to the object and the target object is deformed. On the other hand, correlation filter (CF)-based trackers that use handcrafted features (e.g., HOG features) to spatially locate the target. These two approaches have complementary characteristics due to differences in learning methods, features used, and the size of search regions. Also, we found that these trackers are complementary in terms of performance in benchmarking. Therefore, we propose the “Complementary Tracking framework using Average peak-to-correlation energy” (CTA). CTA is the generic object tracking framework that connects CF-trackers and Siamese-trackers in parallel and exploits the complementary features of these. In CTA, when a tracking failure of the Siamese tracker is detected using Average peak-to-correlation energy (APCE), which is an evaluation index of the response map matrix, the CF-trackers correct the output. In experimental on OTB100, CTA significantly improves the performance over the original tracker for several combinations of Siamese-trackers and CF-rackers.

scholarly journals Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

Coatings ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 152 ◽  
Author(s):  
Zhun Fan ◽  
Chong Li ◽  
Ying Chen ◽  
Paola Di Mascio ◽  
Xiaopeng Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
The Other ◽  
Detection Methods ◽  
Deep Convolutional Neural Networks ◽  
Predicted Probability ◽  
Low Efficiency ◽  
The Individual ◽  
Pavement Crack Detection

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.

scholarly journals Super Sparse Convolutional Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 4440-4447 ◽  
Author(s):  
Yao Lu ◽  
Guangming Lu ◽  
Bob Zhang ◽  
Yuanrong Xu ◽  
Jinxing Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Mobile Networks ◽  
The Other ◽  
Spatial Extent ◽  
Low Resolution ◽  
Feature Maps ◽  
Performance Loss ◽  
Fitting Problem

To construct small mobile networks without performance loss and address the over-fitting issues caused by the less abundant training datasets, this paper proposes a novel super sparse convolutional (SSC) kernel, and its corresponding network is called SSC-Net. In a SSC kernel, every spatial kernel has only one non-zero parameter and these non-zero spatial positions are all different. The SSC kernel can effectively select the pixels from the feature maps according to its non-zero positions and perform on them. Therefore, SSC can preserve the general characteristics of the geometric and the channels’ differences, resulting in preserving the quality of the retrieved features and meeting the general accuracy requirements. Furthermore, SSC can be entirely implemented by the “shift” and “group point-wise” convolutional operations without any spatial kernels (e.g., “3×3”). Therefore, SSC is the first method to remove the parameters’ redundancy from the both spatial extent and the channel extent, leading to largely decreasing the parameters and Flops as well as further reducing the img2col and col2img operations implemented by the low leveled libraries. Meanwhile, SSC-Net can improve the sparsity and overcome the over-fitting more effectively than the other mobile networks. Comparative experiments were performed on the less abundant CIFAR and low resolution ImageNet datasets. The results showed that the SSC-Nets can significantly decrease the parameters and the computational Flops without any performance losses. Additionally, it can also improve the ability of addressing the over-fitting problem on the more challenging less abundant datasets.

scholarly journals Selection of features system and network parameters for hyperspectral images classification using convolutional neural networks

2021 ◽  
Author(s):  
V.I. Kozik ◽  
E.S. Nezhevenko
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Correct Classification ◽  
Network Parameters ◽  
Input Layer ◽  
Selection Of ◽  
Classified Image

A classification system for hyperspectral images using convolutional neural networks is described. A specific network was selected and analyzed. The network parameters, ensured the maximum classification accuracy: dimension of the input layer, number of the layers, size of the fragments into which the classified image is divided, number of learning epochs, are experimentally determined. High percentages of correct classification were obtained with a large-format hyperspectral image, and some of the classes into which the image is divided are very close to each other and, accordingly, are difficult to distinguish by hyperspectra.

scholarly journals Convolutional Neural Networks with Transfer Learning for Recognition of COVID-19: A Comparative Study of Different Approaches

AI ◽  
2020 ◽  
Vol 1 (4) ◽  
pp. 586-606
Author(s):  
Tanmay Garg ◽  
Mamta Garg ◽  
Om Prakash Mahela ◽  
Akhil Ranjan Garg
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Convolutional Neural Networks ◽  
Image Representation ◽  
Principal Component ◽  
Classification Problem ◽  
The Other ◽  
X Ray ◽  
Image Representations

To judge the ability of convolutional neural networks (CNNs) to effectively and efficiently transfer image representations learned on the ImageNet dataset to the task of recognizing COVID-19 in this work, we propose and analyze four approaches. For this purpose, we use VGG16, ResNetV2, InceptionResNetV2, DenseNet121, and MobileNetV2 CNN models pre-trained on ImageNet dataset to extract features from X-ray images of COVID and Non-COVID patients. Simulations study performed by us reveal that these pre-trained models have a different level of ability to transfer image representation. We find that in the approaches that we have proposed, if we use either ResNetV2 or DenseNet121 to extract features, then the performance of these approaches to detect COVID-19 is better. One of the important findings of our study is that the use of principal component analysis for feature selection improves efficiency. The approach using the fusion of features outperforms all the other approaches, and with this approach, we could achieve an accuracy of 0.94 for a three-class classification problem. This work will not only be useful for COVID-19 detection but also for any domain with small datasets.

scholarly journals Separable Convolutional Neural Networks For Facial Expressions Recognition

2021 ◽  
Author(s):  
Andry Chowanda
Keyword(s):  
Neural Networks ◽  
Social Interactions ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
The Other ◽  
Computational Power ◽  
Facial Cues ◽  
Emotional Recognition ◽  
Testing Accuracy ◽  
Facial Expressions Recognition

Abstract Social interactions are important for us, human, as social creatures. Emotions play an important part in social interactions. They usually express meanings along with the spoken utterances to the interlocutors. Automatic facial expressions recognition is one technique to automatically capture, recognise, and understand emotions from the interlocutor. Many techniques proposed to increase the accuracy of emotions recognition from facial cues. Architecture such as convolutional neural networks demonstrates promising results for emotions recognition. However, most of the current models of convolutional neural networks require an enormous computational power to train and process emotional recognition. This research aims to build compact networks with depthwise separable layers while also maintaining performance. Three datasets and three other similar architectures were used to be compared to the proposed architecture. The results show that the proposed architecture performed the best among the other architectures. It achieved up to 13% better accuracy and 6-71% smaller and more compact than the other architectures. The best testing accuracy achieved by the architecture was 99.4%.

Sign in / Sign up

Export Citation Format

Share Document