BdSLW-11: Dataset of Bangladeshi sign language words for recognizing 11 daily useful BdSL words

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Abstract

The dataset of Bangladeshi sign language words (BdSLW) is rare. Though there are lots of datasets of BdSL sign alphabets, numbers, or characters, there are not enough datasets of sign words. This is the first dataset about sign words of BdSL according to the author(s) knowledge. So, this dataset is developed by collecting data from people. This is an image dataset. This dataset is a collection of 1105 images of sign words. A total of 11 sign word categories are selected which are important and daily use in our life. As this is an image dataset, so the images of sign words are taken by camera from the sign users of Bangladesh. Authors have gone to the individuals of sign users and captured images from them with their permission. Then the images are analyzed and segmented into the images which have quality such as no background, clear, bright, etc. This dataset is used for recognizing BdSL sign words.

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A survey on Image Data Augmentation for Deep Learning

Connor Shorten, Taghi Khoshgoftaar (2019)

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Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Mohsen Azimi, Armin Dadras Eslamlou, Gokhan Pekcan (2020)

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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CNN Based on Transfer Learning Models Using Data Augmentation and Transformation for Detection of Concrete Crack

Md. Monirul Islam, Md. Belal Hossain, Md. Nasim Akhtar … (2022)

Cracks in concrete cause initial structural damage to civil infrastructures such as buildings, bridges, and highways, which in turn causes further damage and is thus regarded as a serious safety concern. Early detection of it can assist in preventing further damage and can enable safety in advance by avoiding any possible accident caused while using those infrastructures. Machine learning-based detection is gaining favor over time-consuming classical detection approaches that can only fulfill the objective of early detection. To identify concrete surface cracks from images, this research developed a transfer learning approach (TL) based on Convolutional Neural Networks (CNN). This work employs the transfer learning strategy by leveraging four existing deep learning (DL) models named VGG16, ResNet18, DenseNet161, and AlexNet with pre-trained (trained on ImageNet) weights. To validate the performance of each model, four performance indicators are used: accuracy, recall, precision, and F1-score. Using the publicly available CCIC dataset, the suggested technique on AlexNet outperforms existing models with a testing accuracy of 99.90%, precision of 99.92%, recall of 99.80%, and F1-score of 99.86% for crack class. Our approach is further validated by using an external dataset, BWCI, available on Kaggle. Using BWCI, models VGG16, ResNet18, DenseNet161, and AlexNet achieved the accuracy of 99.90%, 99.60%, 99.80%, and 99.90% respectively. This proposed transfer learning-based method, which is based on the CNN method, is demonstrated to be more effective at detecting cracks in concrete structures and is also applicable to other detection tasks.

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Author and article information

Contributors

Md. Monirul Islam

Journal

Journal ID (nlm-ta): Data Brief

Journal ID (iso-abbrev): Data Brief

Title: Data in Brief

Publisher: Elsevier

ISSN (Electronic): 2352-3409

Publication date PMC-release: 13 November 2022

Publication date Collection: December 2022

Publication date (Electronic): 13 November 2022

Volume: 45

Electronic Location Identifier: 108747

Affiliations

[a ]Department of Computer Science and Engineering, University of Information Technology and Sciences (UITS), Dhaka 1212, Bangladesh

[b ]Department of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT), Mirpur, Dhaka-1216, Bangladesh

[c ]Department of Computer Science and Engineering, Atish Dipankar University of Science & Technology, Dhaka 1230, Bangladesh

[d ]Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur 1707, Bangladesh

Author notes

[* ]Corresponding author. monirul.islam@ 123456uits.edu.bd

Article

Publisher Item ID: S2352-3409(22)00951-9 Publisher ID: 108747

DOI: 10.1016/j.dib.2022.108747

PMC ID: 9679746

PubMed ID: 36425983

SO-VID: 20ae905b-5778-4e16-9e2f-6d42f0f5be3d

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

History

Date received : 30 September 2022

Date revision received : 5 November 2022

Date accepted : 7 November 2022

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BdSLW-11: Dataset of Bangladeshi sign language words for recognizing 11 daily useful BdSL words

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Good practices in (cog-neuro) science and science communication

Most cited references 9

A survey on Image Data Augmentation for Deep Learning

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

CNN Based on Transfer Learning Models Using Data Augmentation and Transformation for Detection of Concrete Crack

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