Year: 2025
Type: article
Abstract: Timely and accurate forest fire monitoring is of vital importance for curbing the spread of fires and reducing ecological and economic losses. Although the development of drones and remote sensing technologies has promoted visual-based fire monitoring research, existing methods still face key challenges in complex natural environments: insufficient detection capabilities for small-scale fire sources/smoke (key indicators of early fires), and high false detection rates for environmental interference (such as similar texture backgrounds). These limitations severely restrict the reliability and practicability of the monitoring system. To address these challenges, this paper proposes a deeply optimized model based on the YOLOv8 architecture. This model adopts an innovative multi-module collaborative design (for the specific structure, please refer to the methods section), aiming to significantly improve the detection accuracy and robustness for small targets and complex interference scenarios, while maintaining high efficiency to meet real-time warning requirements. The verification results show that our method outperforms the benchmark model in terms of accuracy (with a 4.7% improvement in mAP) and false detection rate, demonstrating its effectiveness in addressing the gaps in existing research.
Source: Scientific Reports
Authors: Yunchang Zheng, Pengzhu Guo, Xiaokai Tian, Yunlong Ye
Institutions: Hebei University of Architecture, Hebei North University
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4415468604