Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors

Xiyuan Liu, Lingxiao Wang, Jiahao Li, Khan Raqib Mahmud and Shuo Pang ()
Additional contact information
Xiyuan Liu: Department of Mathematics and Statistics, Louisiana Tech University, Ruston, LA 71272, USA
Lingxiao Wang: Department of Electrical Engineering, Louisiana Tech University, Ruston, LA 71272, USA
Jiahao Li: Department of Mathematics and Statistics, Louisiana Tech University, Ruston, LA 71272, USA
Khan Raqib Mahmud: Department of Computer Science, Louisiana Tech University, Ruston, LA 71272, USA
Shuo Pang: Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA

Mathematics, 2025, vol. 13, issue 7, 1-18

Abstract: 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 ( A R ( p ) ) 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.

Keywords: objective detection; auto-regression; time series analysis; trend estimation; Markov Chain Monte Carlo; Hidden Markov Model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/7/1046/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/7/1046/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:7:p:1046-:d:1618791

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().