Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors - Xiyuan Liu; Lingxiao Wang; Jiahao Li; Khan Raqib Mahmud; Shuo Pang (Mathematics)
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 (<semantics>AR(p)</semantics>) 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.