Improved Lightweight YOLOv11 Algorithm for Real-Time Forest Fire Detection

Modern computer vision techniques for forest fire detection face a trade-off between computational efficiency and detection accuracy in complex forest environments. To address this, we propose a lightweight YOLOv11n-based framework optimized for edge deployment. The backbone network integrates a novel C3k2MBNV2 (Cross Stage Partial Bottleneck with 3 convolutions and kernel size 2 MobileNetV2) block to enable efficient fire feature extraction via a compact architecture. We further introduce the SCDown (Spatial-Channel Decoupled Downsampling) block in both the backbone and neck to preserve critical information during downsampling. The neck further incorporates the C3k2WTDC (Cross Stage Partial Bottleneck with 3 convolutions and kernel size 2, combined with Wavelet Transform Depthwise Convolution) block, enhancing contextual understanding with reduced computational overhead. Experiments on a forest fire dataset demonstrate that our model achieves a 53.2% reduction in parameters and 28.6% fewer FLOPs compared to YOLOv11n (You Only Look Once version eleven), along with a 3.3% improvement in mean average precision. These advancements establish an optimal balance between efficiency and accuracy, enabling the proposed framework to attain real-time detection capabilities on resource-constrained edge devices in forest environments. This work provides a practical solution for deploying reliable forest fire detection systems in scenarios demanding low latency and minimal computational resources.