A forest fire identification and monitoring model based on improved YOLOv8

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.