Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors
In recent years, global weather changes have underscored the importance of wildfire detection, particularly through Uncrewed Aircraft System (UAS)-based smoke detection using Deep Learning (DL) approaches. Among these, object detection algorithms like You Only Look Once version 7 (YOLOv7) have gaine...
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MDPI AG
2025-03-01
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| Series: | Mathematics |
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| author | Xiyuan Liu Lingxiao Wang Jiahao Li Khan Raqib Mahmud Shuo Pang |
| author_facet | Xiyuan Liu Lingxiao Wang Jiahao Li Khan Raqib Mahmud Shuo Pang |
| author_sort | Xiyuan Liu |
| collection | DOAJ |
| description | In recent years, global weather changes have underscored the importance of wildfire detection, particularly through Uncrewed Aircraft System (UAS)-based smoke detection using Deep Learning (DL) approaches. Among these, object detection algorithms like You Only Look Once version 7 (YOLOv7) have gained significant popularity due to their efficiency in identifying objects within images. However, these algorithms face limitations when applied to video feeds, as they treat each frame as an independent image, failing to track objects across consecutive frames. To address this issue, we propose a parametric Markov Chain Monte Carlo (MCMC) trend estimation algorithm that incorporates an Auto-Regressive (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>R</mi><mo>(</mo><mi>p</mi><mo>)</mo></mrow></semantics></math></inline-formula>) error assumption. We demonstrate that this MCMC algorithm achieves stationarity for the AR(p) model under specific constraints. Additionally, as a parametric method, the proposed algorithm can be applied to any time-related data, enabling the detection of underlying causes of trend changes for further analysis. Finally, we show that the proposed method can “stabilize” YOLOv7 detections, serving as an additional step to enhance the original algorithm’s performance. |
| format | Article |
| id | doaj-art-c9d43f1cb43f45dda45f17b7c6592bed |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-c9d43f1cb43f45dda45f17b7c6592bed2025-08-20T03:08:57ZengMDPI AGMathematics2227-73902025-03-01137104610.3390/math13071046Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression ErrorsXiyuan Liu0Lingxiao Wang1Jiahao Li2Khan Raqib Mahmud3Shuo Pang4Department of Mathematics and Statistics, Louisiana Tech University, Ruston, LA 71272, USADepartment of Electrical Engineering, Louisiana Tech University, Ruston, LA 71272, USADepartment of Mathematics and Statistics, Louisiana Tech University, Ruston, LA 71272, USADepartment of Computer Science, Louisiana Tech University, Ruston, LA 71272, USADepartment of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USAIn recent years, global weather changes have underscored the importance of wildfire detection, particularly through Uncrewed Aircraft System (UAS)-based smoke detection using Deep Learning (DL) approaches. Among these, object detection algorithms like You Only Look Once version 7 (YOLOv7) have gained significant popularity due to their efficiency in identifying objects within images. However, these algorithms face limitations when applied to video feeds, as they treat each frame as an independent image, failing to track objects across consecutive frames. To address this issue, we propose a parametric Markov Chain Monte Carlo (MCMC) trend estimation algorithm that incorporates an Auto-Regressive (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>R</mi><mo>(</mo><mi>p</mi><mo>)</mo></mrow></semantics></math></inline-formula>) error assumption. We demonstrate that this MCMC algorithm achieves stationarity for the AR(p) model under specific constraints. Additionally, as a parametric method, the proposed algorithm can be applied to any time-related data, enabling the detection of underlying causes of trend changes for further analysis. Finally, we show that the proposed method can “stabilize” YOLOv7 detections, serving as an additional step to enhance the original algorithm’s performance.https://www.mdpi.com/2227-7390/13/7/1046objective detectionauto-regressiontime series analysistrend estimationMarkov Chain Monte CarloHidden Markov Model |
| spellingShingle | Xiyuan Liu Lingxiao Wang Jiahao Li Khan Raqib Mahmud Shuo Pang Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors Mathematics objective detection auto-regression time series analysis trend estimation Markov Chain Monte Carlo Hidden Markov Model |
| title | Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors |
| title_full | Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors |
| title_fullStr | Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors |
| title_full_unstemmed | Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors |
| title_short | Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors |
| title_sort | enhancing wildfire detection via trend estimation under auto regression errors |
| topic | objective detection auto-regression time series analysis trend estimation Markov Chain Monte Carlo Hidden Markov Model |
| url | https://www.mdpi.com/2227-7390/13/7/1046 |
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