An improved fire and smoke detection method based on YOLOv8n for smart factories
Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180464 |
| _version_ | 1826115726337900544 |
|---|---|
| author | Zhang, Ziyang Tan, Lingye Tiong, Robert Lee Kong |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Zhang, Ziyang Tan, Lingye Tiong, Robert Lee Kong |
| author_sort | Zhang, Ziyang |
| collection | NTU |
| description | Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In addition, lots of studies focus on fire detection, while smoke, the important derivative of a fire disaster, is not detected by such algorithms. To better help smart factories monitor fire disasters, this paper proposes an improved fire and smoke detection method based on YOLOv8n. To ensure the quality of the algorithm and training process, a self-made dataset including more than 5000 images and their corresponding labels is created. Then, nine advanced algorithms are selected and tested on the dataset. YOLOv8n exhibits the best detection results in terms of accuracy and detection speed. ConNeXtV2 is then inserted into the backbone to enhance inter-channel feature competition. RepBlock and SimConv are selected to replace the original Conv and improve computational ability and memory bandwidth. For the loss function, CIoU is replaced by MPDIoU to ensure an efficient and accurate bounding box. Ablation tests show that our improved algorithm achieves better performance in all four metrics reflecting accuracy: precision, recall, F1, and mAP@50. Compared with the original model, whose four metrics are approximately 90%, the modified algorithm achieves above 95%. mAP@50 in particular reaches 95.6%, exhibiting an improvement of approximately 4.5%. Although complexity improves, the requirements of real-time fire and smoke monitoring are satisfied. |
| first_indexed | 2025-03-09T11:27:46Z |
| format | Journal Article |
| id | ntu-10356/180464 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T11:27:46Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1804642024-10-11T15:33:43Z An improved fire and smoke detection method based on YOLOv8n for smart factories Zhang, Ziyang Tan, Lingye Tiong, Robert Lee Kong School of Civil and Environmental Engineering Engineering Factory fire and smoke detection YOLOv8n Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In addition, lots of studies focus on fire detection, while smoke, the important derivative of a fire disaster, is not detected by such algorithms. To better help smart factories monitor fire disasters, this paper proposes an improved fire and smoke detection method based on YOLOv8n. To ensure the quality of the algorithm and training process, a self-made dataset including more than 5000 images and their corresponding labels is created. Then, nine advanced algorithms are selected and tested on the dataset. YOLOv8n exhibits the best detection results in terms of accuracy and detection speed. ConNeXtV2 is then inserted into the backbone to enhance inter-channel feature competition. RepBlock and SimConv are selected to replace the original Conv and improve computational ability and memory bandwidth. For the loss function, CIoU is replaced by MPDIoU to ensure an efficient and accurate bounding box. Ablation tests show that our improved algorithm achieves better performance in all four metrics reflecting accuracy: precision, recall, F1, and mAP@50. Compared with the original model, whose four metrics are approximately 90%, the modified algorithm achieves above 95%. mAP@50 in particular reaches 95.6%, exhibiting an improvement of approximately 4.5%. Although complexity improves, the requirements of real-time fire and smoke monitoring are satisfied. Published version 2024-10-08T05:43:11Z 2024-10-08T05:43:11Z 2024 Journal Article Zhang, Z., Tan, L. & Tiong, R. L. K. (2024). An improved fire and smoke detection method based on YOLOv8n for smart factories. Sensors, 24(15), 4786-. https://dx.doi.org/10.3390/s24154786 1424-8220 https://hdl.handle.net/10356/180464 10.3390/s24154786 39123833 2-s2.0-85200840448 15 24 4786 en Sensors © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
| spellingShingle | Engineering Factory fire and smoke detection YOLOv8n Zhang, Ziyang Tan, Lingye Tiong, Robert Lee Kong An improved fire and smoke detection method based on YOLOv8n for smart factories |
| title | An improved fire and smoke detection method based on YOLOv8n for smart factories |
| title_full | An improved fire and smoke detection method based on YOLOv8n for smart factories |
| title_fullStr | An improved fire and smoke detection method based on YOLOv8n for smart factories |
| title_full_unstemmed | An improved fire and smoke detection method based on YOLOv8n for smart factories |
| title_short | An improved fire and smoke detection method based on YOLOv8n for smart factories |
| title_sort | improved fire and smoke detection method based on yolov8n for smart factories |
| topic | Engineering Factory fire and smoke detection YOLOv8n |
| url | https://hdl.handle.net/10356/180464 |
| work_keys_str_mv | AT zhangziyang animprovedfireandsmokedetectionmethodbasedonyolov8nforsmartfactories AT tanlingye animprovedfireandsmokedetectionmethodbasedonyolov8nforsmartfactories AT tiongrobertleekong animprovedfireandsmokedetectionmethodbasedonyolov8nforsmartfactories AT zhangziyang improvedfireandsmokedetectionmethodbasedonyolov8nforsmartfactories AT tanlingye improvedfireandsmokedetectionmethodbasedonyolov8nforsmartfactories AT tiongrobertleekong improvedfireandsmokedetectionmethodbasedonyolov8nforsmartfactories |