Audio-Based Wildfire Detection on Embedded Systems
The occurrence of wildfires often results in significant fatalities. As wildfires are notorious for their high speed of spread, the ability to identify wildfire at its early stage is essential in quickly obtaining control of the fire and in reducing property loss and preventing loss of life. This wo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/9/1417 |
| _version_ | 1797505171338559488 |
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| author | Hung-Tien Huang Austin R. J. Downey Jason D. Bakos |
| author_facet | Hung-Tien Huang Austin R. J. Downey Jason D. Bakos |
| author_sort | Hung-Tien Huang |
| collection | DOAJ |
| description | The occurrence of wildfires often results in significant fatalities. As wildfires are notorious for their high speed of spread, the ability to identify wildfire at its early stage is essential in quickly obtaining control of the fire and in reducing property loss and preventing loss of life. This work presents a machine learning wildfire detecting data pipeline that can be deployed on embedded systems in remote locations. The proposed data pipeline consists of three main steps: audio preprocessing, feature engineering, and classification. Experiments show that the proposed data pipeline is capable of detecting wildfire effectively with high precision and is capable of detecting wildfire sound over the forest’s background soundscape. When being deployed on a Raspberry Pi 4, the proposed data pipeline takes 66 milliseconds to process a 1 s sound clip. To the knowledge of the author, this is the first edge-computing implementation of an audio-based wildfire detection system. |
| first_indexed | 2024-03-10T04:14:48Z |
| format | Article |
| id | doaj.art-2a39b57705964719af52ade916697027 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T04:14:48Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-2a39b57705964719af52ade9166970272023-11-23T08:03:19ZengMDPI AGElectronics2079-92922022-04-01119141710.3390/electronics11091417Audio-Based Wildfire Detection on Embedded SystemsHung-Tien Huang0Austin R. J. Downey1Jason D. Bakos2Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USADepartment of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USADepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USAThe occurrence of wildfires often results in significant fatalities. As wildfires are notorious for their high speed of spread, the ability to identify wildfire at its early stage is essential in quickly obtaining control of the fire and in reducing property loss and preventing loss of life. This work presents a machine learning wildfire detecting data pipeline that can be deployed on embedded systems in remote locations. The proposed data pipeline consists of three main steps: audio preprocessing, feature engineering, and classification. Experiments show that the proposed data pipeline is capable of detecting wildfire effectively with high precision and is capable of detecting wildfire sound over the forest’s background soundscape. When being deployed on a Raspberry Pi 4, the proposed data pipeline takes 66 milliseconds to process a 1 s sound clip. To the knowledge of the author, this is the first edge-computing implementation of an audio-based wildfire detection system.https://www.mdpi.com/2079-9292/11/9/1417wildfire detectionacoustic signal detectionembedded system |
| spellingShingle | Hung-Tien Huang Austin R. J. Downey Jason D. Bakos Audio-Based Wildfire Detection on Embedded Systems Electronics wildfire detection acoustic signal detection embedded system |
| title | Audio-Based Wildfire Detection on Embedded Systems |
| title_full | Audio-Based Wildfire Detection on Embedded Systems |
| title_fullStr | Audio-Based Wildfire Detection on Embedded Systems |
| title_full_unstemmed | Audio-Based Wildfire Detection on Embedded Systems |
| title_short | Audio-Based Wildfire Detection on Embedded Systems |
| title_sort | audio based wildfire detection on embedded systems |
| topic | wildfire detection acoustic signal detection embedded system |
| url | https://www.mdpi.com/2079-9292/11/9/1417 |
| work_keys_str_mv | AT hungtienhuang audiobasedwildfiredetectiononembeddedsystems AT austinrjdowney audiobasedwildfiredetectiononembeddedsystems AT jasondbakos audiobasedwildfiredetectiononembeddedsystems |