Forest fires cause severe ecological and economic losses, so timely and accurate detection becomes crucial for effective prevention and control. Edge devices with intelligent algorithms can detect forest fires in real time. Current deep learning algorithms can achieve high accuracy, but they are not suitable for edge devices because they require substantial computing resources. To address this issue, this study proposes a real-time lightweight forest fire detection network (RLFNet) improved from YOLOv11n, with three key enhancements to the backbone, neck, and head. (1) A Parallel Multi-Scale Extraction Block (PMEB) improves C3k2 with a dual-branch parallel strategy to enhance multi-scale feature extraction efficiency; (2) a Bidirectional Cross Fusion Module (BCFM) replaces simple Concat with a context-aware cross-gating mechanism to suppress background noise and reduce false alarms; and (3) a Faster Inference Detection Head (FIDH) leverages structural re-parameterization and group normalization to boost inference efficiency while reducing parameters. In addition, a Layer-Adaptive Magnitude-based Pruning (LAMP) strategy is applied to further improve model’s computational efficiency. Experimental results on the self-constructed Diverse Fire Scenario (DFS) dataset demonstrate that RLFNet reduces parameters and GFLOPs by 25.2% and 20.6%, boosts mAP50 by 5.3%, and achieves an inference speed of 225 FPS, attaining the best accuracy and speed among the compared models. Validation on a public remote sensing dataset further confirms its strong generalization. These results indicate that RLFNet provides a high efficiency and lightweight solution for edge devices to real-time detect forest fires.
Huang et al. (2026) studied this question.