Automatic helmet-less biker detection using deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.
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Brac University
2024
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| Online pristup: | http://hdl.handle.net/10361/23669 |
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10361-236692024-07-04T21:04:18Z Automatic helmet-less biker detection using deep learning Ratul, Md. Ibrahim Mohon, Abdul Karim Ibne Sarker, Md. Reduan Rasel, Annajiat Alim Department of Computer Science and Engineering, Brac University Helmet CNN LSTM Attention Data mining Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 38-41). When riders don’t wear a helmet while driving a motorcycle, they aren’t paying attention, leading to crashes and more deaths. The researchers utilized multiple deep-learning models to identify motorcyclists without helmets. We have to identify correctly if a rider is wearing a helmet as we want to reduce risks regarding not wearing a helmet. Our paper proposes a convolutional neural network-based way to decide whether the rider carries a helmet. We utilized pre-trained deep learning models to forecast the outcome because we used a customized dataset. These models include EfficientNetB0, Inception, ResNet50, VGG16, and VGG19, and the results are satisfactory. Later, we put forth our model, combining the CNN, LSTM, and attention models. Our fusion model’s foundation is a Dialated CNN layer. Our dilated CNN layer comprises three maximum pool layers and five convolutional layers. LSTM and an attention model layer follow convolutional layers on a fivelayer CNN. Additionally, we used the same model to predict three classes on a separate dataset, and both models produced satisfactory outcomes. Our goal is to make greater use of the deep learning technique so that it can detect with incredible speed and precision. The test results indicate that, with a classification accuracy of 92.41%, our proposed method outperforms the alternatives we used. We have used YOLOV8 to detect riders wearing non-helmet headwear, such as caps, hijabs, and turbans, and have classified them as non-helmet wearers with satisfactory results. Md. Ibrahim Ratul Abdul Karim Ibne Mohon Md. Reduan Sarker B.Sc. in Computer Science 2024-07-04T06:06:22Z 2024-07-04T06:06:22Z © 2023 2023-09 Thesis ID 18301113 ID 18301152 ID 18301088 http://hdl.handle.net/10361/23669 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 51 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Helmet CNN LSTM Attention Data mining Neural networks (Computer science) |
| spellingShingle |
Helmet CNN LSTM Attention Data mining Neural networks (Computer science) Ratul, Md. Ibrahim Mohon, Abdul Karim Ibne Sarker, Md. Reduan Automatic helmet-less biker detection using deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. |
| author2 |
Rasel, Annajiat Alim |
| author_facet |
Rasel, Annajiat Alim Ratul, Md. Ibrahim Mohon, Abdul Karim Ibne Sarker, Md. Reduan |
| format |
Thesis |
| author |
Ratul, Md. Ibrahim Mohon, Abdul Karim Ibne Sarker, Md. Reduan |
| author_sort |
Ratul, Md. Ibrahim |
| title |
Automatic helmet-less biker detection using deep learning |
| title_short |
Automatic helmet-less biker detection using deep learning |
| title_full |
Automatic helmet-less biker detection using deep learning |
| title_fullStr |
Automatic helmet-less biker detection using deep learning |
| title_full_unstemmed |
Automatic helmet-less biker detection using deep learning |
| title_sort |
automatic helmet-less biker detection using deep learning |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23669 |
| work_keys_str_mv |
AT ratulmdibrahim automatichelmetlessbikerdetectionusingdeeplearning AT mohonabdulkarimibne automatichelmetlessbikerdetectionusingdeeplearning AT sarkermdreduan automatichelmetlessbikerdetectionusingdeeplearning |
| _version_ |
1814309238206365696 |