Image analytics using artificial intelligence (fire and smoke detection)
With the cost of human labor on-site increasing annually, faster and more accurate technology for detecting fire without constant supervision is necessary. Specifically, this report will use a YOLO (you only look once) model powered by Artificial Intelligence (AI). Currently, YOLO has 11 versions,...
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2024
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sg-ntu-dr.10356-1817942024-12-20T15:45:56Z Image analytics using artificial intelligence (fire and smoke detection) Mah, Chi Ming Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering With the cost of human labor on-site increasing annually, faster and more accurate technology for detecting fire without constant supervision is necessary. Specifically, this report will use a YOLO (you only look once) model powered by Artificial Intelligence (AI). Currently, YOLO has 11 versions, and we will be using YOLO V10 as the base model in this project. This system uses convolutional neural networks (CNNS) to process and analyze real-time video feeds from surveillance cameras. The proposed method offers several advantages, including detecting fires at their earliest stages. By training the AI model on a comprehensive dataset containing diverse fire and smoke scenarios, the system learns to identify patterns and characteristics of fire and smoke. The system will detect fire or smoke without the presence of conventional smoke detectors, which can only be installed in enclosed environments. Although false positive results may be present, the threat of fire is too significant to ignore for anyone's safety. This will enhance fire safety measures and solutions for users, paving the way for more intelligent and more efficient fire detection and prevention approaches. Bachelor's degree 2024-12-18T13:10:48Z 2024-12-18T13:10:48Z 2024 Final Year Project (FYP) Mah, C. M. (2024). Image analytics using artificial intelligence (fire and smoke detection). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181794 https://hdl.handle.net/10356/181794 en P3005-231 application/pdf Nanyang Technological University |
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With the cost of human labor on-site increasing annually, faster and more accurate technology for detecting fire without constant supervision is necessary.
Specifically, this report will use a YOLO (you only look once) model powered by Artificial Intelligence (AI). Currently, YOLO has 11 versions, and we will be using YOLO V10 as the base model in this project. This system uses convolutional neural networks (CNNS) to process and analyze real-time video feeds from surveillance cameras. The proposed method offers several advantages, including detecting fires at their earliest stages. By training the AI model on a comprehensive dataset containing diverse fire and smoke scenarios, the system learns to identify patterns and characteristics of fire and smoke.
The system will detect fire or smoke without the presence of conventional smoke detectors, which can only be installed in enclosed environments. Although false positive results may be present, the threat of fire is too significant to ignore for anyone's safety. This will enhance fire safety measures and solutions for users, paving the way for more intelligent and more efficient fire detection and prevention approaches. |
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Yap Kim Hui |
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Yap Kim Hui Mah, Chi Ming |
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Final Year Project |
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Mah, Chi Ming |
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Mah, Chi Ming |
title |
Image analytics using artificial intelligence (fire and smoke detection) |
title_short |
Image analytics using artificial intelligence (fire and smoke detection) |
title_full |
Image analytics using artificial intelligence (fire and smoke detection) |
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Image analytics using artificial intelligence (fire and smoke detection) |
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Image analytics using artificial intelligence (fire and smoke detection) |
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image analytics using artificial intelligence (fire and smoke detection) |
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Nanyang Technological University |
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2024 |
url |
https://hdl.handle.net/10356/181794 |
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1819113021007462400 |