Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5
One of the major responsibilities for forest police is forest fire prevention and forecasting; therefore, accurate and timely fire detection is of great importance and significance. We compared several deep learning networks based on the You Only Look Once (YOLO) framework to detect forest flames wi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/6/7/279 |
| _version_ | 1827733082737737728 |
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| author | Haiqing Liu Heping Hu Fang Zhou Huaping Yuan |
| author_facet | Haiqing Liu Heping Hu Fang Zhou Huaping Yuan |
| author_sort | Haiqing Liu |
| collection | DOAJ |
| description | One of the major responsibilities for forest police is forest fire prevention and forecasting; therefore, accurate and timely fire detection is of great importance and significance. We compared several deep learning networks based on the You Only Look Once (YOLO) framework to detect forest flames with the help of unmanned aerial vehicle (UAV) imagery. We used the open datasets of the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) to train the YOLOv5 and its sub-versions, together with YOLOv3 and YOLOv4, under equal conditions. The results show that the YOLOv5n model can achieve a detection speed of 1.4 ms per frame, which is higher than that of all the other models. Furthermore, the algorithm achieves an average accuracy of 91.4%. Although this value is slightly lower than that of YOLOv5s, it achieves a trade-off between high accuracy and real-time. YOLOv5n achieved a good flame detection effect in the different forest scenes we set. It can detect small target flames on the ground, it can detect fires obscured by trees or disturbed by the environment (such as smoke), and it can also accurately distinguish targets that are similar to flames. Our future work will focus on improving the YOLOv5n model so that it can be deployed directly on UAV for truly real-time and high-precision forest flame detection. Our study provides a new solution to the early prevention of forest fires at small scales, helping forest police make timely and correct decisions. |
| first_indexed | 2024-03-11T01:06:02Z |
| format | Article |
| id | doaj.art-c79c49dbbe5a4a5c8d48c6b153ec04b3 |
| institution | Directory Open Access Journal |
| issn | 2571-6255 |
| language | English |
| last_indexed | 2024-03-11T01:06:02Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj.art-c79c49dbbe5a4a5c8d48c6b153ec04b32023-11-18T19:17:48ZengMDPI AGFire2571-62552023-07-016727910.3390/fire6070279Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5Haiqing Liu0Heping Hu1Fang Zhou2Huaping Yuan3Hunan Police Academy, Changsha 410081, ChinaHunan Police Academy, Changsha 410081, ChinaHunan Police Academy, Changsha 410081, ChinaHunan Police Academy, Changsha 410081, ChinaOne of the major responsibilities for forest police is forest fire prevention and forecasting; therefore, accurate and timely fire detection is of great importance and significance. We compared several deep learning networks based on the You Only Look Once (YOLO) framework to detect forest flames with the help of unmanned aerial vehicle (UAV) imagery. We used the open datasets of the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) to train the YOLOv5 and its sub-versions, together with YOLOv3 and YOLOv4, under equal conditions. The results show that the YOLOv5n model can achieve a detection speed of 1.4 ms per frame, which is higher than that of all the other models. Furthermore, the algorithm achieves an average accuracy of 91.4%. Although this value is slightly lower than that of YOLOv5s, it achieves a trade-off between high accuracy and real-time. YOLOv5n achieved a good flame detection effect in the different forest scenes we set. It can detect small target flames on the ground, it can detect fires obscured by trees or disturbed by the environment (such as smoke), and it can also accurately distinguish targets that are similar to flames. Our future work will focus on improving the YOLOv5n model so that it can be deployed directly on UAV for truly real-time and high-precision forest flame detection. Our study provides a new solution to the early prevention of forest fires at small scales, helping forest police make timely and correct decisions.https://www.mdpi.com/2571-6255/6/7/279forest fireforest policeflame detectionYOLOv5UAV imagerydeep learning |
| spellingShingle | Haiqing Liu Heping Hu Fang Zhou Huaping Yuan Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5 Fire forest fire forest police flame detection YOLOv5 UAV imagery deep learning |
| title | Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5 |
| title_full | Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5 |
| title_fullStr | Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5 |
| title_full_unstemmed | Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5 |
| title_short | Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5 |
| title_sort | forest flame detection in unmanned aerial vehicle imagery based on yolov5 |
| topic | forest fire forest police flame detection YOLOv5 UAV imagery deep learning |
| url | https://www.mdpi.com/2571-6255/6/7/279 |
| work_keys_str_mv | AT haiqingliu forestflamedetectioninunmannedaerialvehicleimagerybasedonyolov5 AT hepinghu forestflamedetectioninunmannedaerialvehicleimagerybasedonyolov5 AT fangzhou forestflamedetectioninunmannedaerialvehicleimagerybasedonyolov5 AT huapingyuan forestflamedetectioninunmannedaerialvehicleimagerybasedonyolov5 |