Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning
The number of accidental fires in buildings has been significantly increased in recent years in Saudi Arabia. Fire Safety Equipment (FSE) plays a crucial role in reducing fire risks. However, this equipment is prone to defects and requires periodic checks and maintenance. Fire safety inspectors are...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2024-04-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6753 |
| _version_ | 1797226265496780800 |
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| author | Asmaa Alayed Rehab Alidrisi Ekram Feras Shahad Aboukozzana Alaa Alomayri |
| author_facet | Asmaa Alayed Rehab Alidrisi Ekram Feras Shahad Aboukozzana Alaa Alomayri |
| author_sort | Asmaa Alayed |
| collection | DOAJ |
| description | The number of accidental fires in buildings has been significantly increased in recent years in Saudi Arabia. Fire Safety Equipment (FSE) plays a crucial role in reducing fire risks. However, this equipment is prone to defects and requires periodic checks and maintenance. Fire safety inspectors are responsible for visual inspection of safety equipment and reporting defects. As the traditional approach of manually checking each piece of equipment can be time-consuming and inaccurate, this study aims to improve the inspection processes of safety equipment. Using computer vision and deep learning techniques, a detection model was trained to visually inspect fire extinguishers and identify defects. Fire extinguisher images were collected, annotated, and augmented to create a dataset of 7,633 images with 16,092 labeled instances. Then, experiments were carried out using YOLOv5, YOLOv7, YOLOv8, and RT-DETR. Pre-trained models were used for transfer learning. A comparative analysis was performed to evaluate these models in terms of accuracy, speed, and model size. The results of YOLOv5n, YOLOv7, YOLOv8n, YOLOv8m, and RT-DETR indicated satisfactory accuracy, ranging between 83.1% and 87.2%. YOLOv8n was chosen as the most suitable due to its fastest inference time of 2.7 ms, its highest mAP0.5 of 87.2%, and its compact model size, making it ideal for real-time mobile applications.
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| first_indexed | 2024-04-24T14:22:10Z |
| format | Article |
| id | doaj.art-101772aed1df45e0bb30c2176a88c3e6 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-04-24T14:22:10Z |
| publishDate | 2024-04-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-101772aed1df45e0bb30c2176a88c3e62024-04-03T06:14:20ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362024-04-0114210.48084/etasr.6753Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep LearningAsmaa Alayed0Rehab Alidrisi1Ekram Feras2Shahad Aboukozzana3Alaa Alomayri4College of Computing, Umm Al-Qura University, Saudi ArabiaCollege of Computing, Umm Al-Qura University, Saudi ArabiaCollege of Computing, Umm Al-Qura University, Saudi ArabiaCollege of Computing, Umm Al-Qura University, Saudi ArabiaCollege of Computing, Umm Al-Qura University, Saudi ArabiaThe number of accidental fires in buildings has been significantly increased in recent years in Saudi Arabia. Fire Safety Equipment (FSE) plays a crucial role in reducing fire risks. However, this equipment is prone to defects and requires periodic checks and maintenance. Fire safety inspectors are responsible for visual inspection of safety equipment and reporting defects. As the traditional approach of manually checking each piece of equipment can be time-consuming and inaccurate, this study aims to improve the inspection processes of safety equipment. Using computer vision and deep learning techniques, a detection model was trained to visually inspect fire extinguishers and identify defects. Fire extinguisher images were collected, annotated, and augmented to create a dataset of 7,633 images with 16,092 labeled instances. Then, experiments were carried out using YOLOv5, YOLOv7, YOLOv8, and RT-DETR. Pre-trained models were used for transfer learning. A comparative analysis was performed to evaluate these models in terms of accuracy, speed, and model size. The results of YOLOv5n, YOLOv7, YOLOv8n, YOLOv8m, and RT-DETR indicated satisfactory accuracy, ranging between 83.1% and 87.2%. YOLOv8n was chosen as the most suitable due to its fastest inference time of 2.7 ms, its highest mAP0.5 of 87.2%, and its compact model size, making it ideal for real-time mobile applications. https://etasr.com/index.php/ETASR/article/view/6753Fire Safety Equipment (FSE)fire safety inspectionvisual inspectiondeep learningcomputer visionobject detection |
| spellingShingle | Asmaa Alayed Rehab Alidrisi Ekram Feras Shahad Aboukozzana Alaa Alomayri Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning Engineering, Technology & Applied Science Research Fire Safety Equipment (FSE) fire safety inspection visual inspection deep learning computer vision object detection |
| title | Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning |
| title_full | Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning |
| title_fullStr | Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning |
| title_full_unstemmed | Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning |
| title_short | Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning |
| title_sort | real time inspection of fire safety equipment using computer vision and deep learning |
| topic | Fire Safety Equipment (FSE) fire safety inspection visual inspection deep learning computer vision object detection |
| url | https://etasr.com/index.php/ETASR/article/view/6753 |
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