LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework

Background Extreme wildfires pose a serious threat to forest vegetation and human life because they spread more rapidly and are more intense than conventional wildfires. Detecting extreme wildfires is challenging due to their visual similarities to traditional fires, and existing models primarily detect the presence or absence of fires without focusing on distinguishing extreme wildfires and providing warnings.Aims To test a system for real time detection of four extreme wildfires.Methods We proposed a novel lightweight model, called LEF-YOLO, based on the YOLOv5 framework. To make the model lightweight, we introduce the bottleneck structure of MobileNetv3 and use depthwise separable convolution instead of conventional convolution. To improve the model's detection accuracy, we apply a multiscale feature fusion strategy and use a Coordinate Attention and Spatial Pyramid Pooling-Fast block to enhance feature extraction.Key results The LEF-YOLO model outperformed the comparison model on the extreme wildfire dataset we constructed, with our model having excellent performance of 2.7 GFLOPs, 61 FPS and 87.9% mAP.Conclusions The detection speed and accuracy of LEF-YOLO can be utilised for the real-time detection of four extreme wildfires in forest fire scenes.Implications The system can facilitate fire control decision-making and foster the intersection between fire science and computer science. We tested a lightweight architecture called LEF-YOLO for detecting four extreme wildfires. We found improved detection accuracy through multi-scale fusion and attention mechanism, and constructed four extreme wildfire datasets and compared these with multiple object detection models and lightweight feature extraction networks. This method is beneficial for the development of extreme wildfire field robots.