Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection
INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and light...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2025-03-01
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| Series: | EAI Endorsed Transactions on Industrial Networks and Intelligent Systems |
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| Online Access: | https://publications.eai.eu/index.php/inis/article/view/7612 |
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| author | Amrutha Annadurai Manas Ranjan Prusty Trilok Nath Pandey Subhra Rani Patra |
| author_facet | Amrutha Annadurai Manas Ranjan Prusty Trilok Nath Pandey Subhra Rani Patra |
| author_sort | Amrutha Annadurai |
| collection | DOAJ |
| description | INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety.
OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and lighting conditions. This dataset has two classes namely with helmet and without helmet.
METHODS: The proposed helmet classification approach utilizes the Multi-Scale Deep Convolutional Neural Network (CNN) framework cascaded with Long Short-Term Memory (LSTM) network. Initially the Multi-Scale Deep CNN extracts modes by applying Single-level Discrete 2D Wavelet Transform (dwt2) to decompose the original images. In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. After feeding the segmented images into a Multi-Scale Deep CNN model, it is cascaded with an LSTM network.
RESULTS: The proposed model achieved accuracies of 99.20% and 95.99% using both 5-Fold Cross-Validation (CV) and Hold-out CV methods, respectively.
CONCLUSION: This result was better than the CNN-LSTM, dwt2-LSTM and a tailor made CNN model.
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| format | Article |
| id | doaj-art-6fe4f8d3ca08476597fffafa3fdbddd5 |
| institution | DOAJ |
| issn | 2410-0218 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Industrial Networks and Intelligent Systems |
| spelling | doaj-art-6fe4f8d3ca08476597fffafa3fdbddd52025-08-20T02:58:37ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Industrial Networks and Intelligent Systems2410-02182025-03-0112210.4108/eetinis.v12i2.7612Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet DetectionAmrutha Annadurai0Manas Ranjan Prusty1Trilok Nath Pandey2Subhra Rani Patra3Vellore Institute of Technology University Vellore Institute of Technology UniversityVellore Institute of Technology University The University of Texas at Arlington INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and lighting conditions. This dataset has two classes namely with helmet and without helmet. METHODS: The proposed helmet classification approach utilizes the Multi-Scale Deep Convolutional Neural Network (CNN) framework cascaded with Long Short-Term Memory (LSTM) network. Initially the Multi-Scale Deep CNN extracts modes by applying Single-level Discrete 2D Wavelet Transform (dwt2) to decompose the original images. In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. After feeding the segmented images into a Multi-Scale Deep CNN model, it is cascaded with an LSTM network. RESULTS: The proposed model achieved accuracies of 99.20% and 95.99% using both 5-Fold Cross-Validation (CV) and Hold-out CV methods, respectively. CONCLUSION: This result was better than the CNN-LSTM, dwt2-LSTM and a tailor made CNN model. https://publications.eai.eu/index.php/inis/article/view/7612Multi Scale CNNLong Short-Term MemoryDiscrete Wavelet TransformHelmet Detection |
| spellingShingle | Amrutha Annadurai Manas Ranjan Prusty Trilok Nath Pandey Subhra Rani Patra Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Multi Scale CNN Long Short-Term Memory Discrete Wavelet Transform Helmet Detection |
| title | Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection |
| title_full | Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection |
| title_fullStr | Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection |
| title_full_unstemmed | Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection |
| title_short | Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection |
| title_sort | single level discrete two dimensional wavelet transform based multiscale deep learning framework for two wheeler helmet detection |
| topic | Multi Scale CNN Long Short-Term Memory Discrete Wavelet Transform Helmet Detection |
| url | https://publications.eai.eu/index.php/inis/article/view/7612 |
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