ATSS-Driven Surface Flame Detection and Extent Evaluation Using Edge Computing on UAVs
The current approach to flame inspection relies mainly on manual methods, resulting in delayed flame detection and inaccurate evaluation of flame extent. In this paper, we propose a surface flame detection model that can be deployed on edge computing devices. The model is based on the ATSS model and...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10184016/ |
| _version_ | 1797774903224565760 |
|---|---|
| author | Wenyin Tao Feng An |
| author_facet | Wenyin Tao Feng An |
| author_sort | Wenyin Tao |
| collection | DOAJ |
| description | The current approach to flame inspection relies mainly on manual methods, resulting in delayed flame detection and inaccurate evaluation of flame extent. In this paper, we propose a surface flame detection model that can be deployed on edge computing devices. The model is based on the ATSS model and has been fine-tuned for this purpose. By equipping a quadcopter drone with the edge computing device carrying this model, real-time flame inspection can be conducted in various environments such as forests and urban areas. Additionally, we introduce a simple and feasible method for flame extent evaluation, which has been overlooked in previous studies. This method utilizes the flame detection results and the camera on the quadcopter drone to calculate the actual area of the flame, providing valuable data support for rescue teams. Experimental results demonstrate that our proposed method surpasses six comparison models, achieving state-of-the-art performance with mAP, AP_50, and AP_75 scores of 0.685, 0.927, and 0.763, respectively. Moreover, the model achieves an FPS of 20.2, meeting the real-time requirements of flame inspection. The proposed flame extent evaluation method accurately estimates the actual area of the flame. |
| first_indexed | 2024-03-12T22:27:55Z |
| format | Article |
| id | doaj.art-b307f6cb3d884677a56e7a9b7a52157f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T22:27:55Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b307f6cb3d884677a56e7a9b7a52157f2023-07-21T23:00:42ZengIEEEIEEE Access2169-35362023-01-0111721087211910.1109/ACCESS.2023.329569710184016ATSS-Driven Surface Flame Detection and Extent Evaluation Using Edge Computing on UAVsWenyin Tao0https://orcid.org/0009-0003-5134-3839Feng An1School of Intelligent Management, Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaThe current approach to flame inspection relies mainly on manual methods, resulting in delayed flame detection and inaccurate evaluation of flame extent. In this paper, we propose a surface flame detection model that can be deployed on edge computing devices. The model is based on the ATSS model and has been fine-tuned for this purpose. By equipping a quadcopter drone with the edge computing device carrying this model, real-time flame inspection can be conducted in various environments such as forests and urban areas. Additionally, we introduce a simple and feasible method for flame extent evaluation, which has been overlooked in previous studies. This method utilizes the flame detection results and the camera on the quadcopter drone to calculate the actual area of the flame, providing valuable data support for rescue teams. Experimental results demonstrate that our proposed method surpasses six comparison models, achieving state-of-the-art performance with mAP, AP_50, and AP_75 scores of 0.685, 0.927, and 0.763, respectively. Moreover, the model achieves an FPS of 20.2, meeting the real-time requirements of flame inspection. The proposed flame extent evaluation method accurately estimates the actual area of the flame.https://ieeexplore.ieee.org/document/10184016/Flame extent evaluationobject detectionedge computingUAV |
| spellingShingle | Wenyin Tao Feng An ATSS-Driven Surface Flame Detection and Extent Evaluation Using Edge Computing on UAVs IEEE Access Flame extent evaluation object detection edge computing UAV |
| title | ATSS-Driven Surface Flame Detection and Extent Evaluation Using Edge Computing on UAVs |
| title_full | ATSS-Driven Surface Flame Detection and Extent Evaluation Using Edge Computing on UAVs |
| title_fullStr | ATSS-Driven Surface Flame Detection and Extent Evaluation Using Edge Computing on UAVs |
| title_full_unstemmed | ATSS-Driven Surface Flame Detection and Extent Evaluation Using Edge Computing on UAVs |
| title_short | ATSS-Driven Surface Flame Detection and Extent Evaluation Using Edge Computing on UAVs |
| title_sort | atss driven surface flame detection and extent evaluation using edge computing on uavs |
| topic | Flame extent evaluation object detection edge computing UAV |
| url | https://ieeexplore.ieee.org/document/10184016/ |
| work_keys_str_mv | AT wenyintao atssdrivensurfaceflamedetectionandextentevaluationusingedgecomputingonuavs AT fengan atssdrivensurfaceflamedetectionandextentevaluationusingedgecomputingonuavs |