YOLOGX: an improved forest fire detection algorithm based on YOLOv8 - Li, Caixiong; Du, Yue; Zhang, Xing; Wu, Peng (Frontiers in Environmental Science, 2025)
To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest fire detection algorithms, we developed a high-precision algorithm, YOLOGX. YOLOGX integrates three pivotal technologies: First, the GD mechanism fuses and extracts features from multi-scale information, significantly enhancing the detection capability for fire targets of varying sizes. Second, the SE-ResNeXt module is integrated into the detection head, optimizing feature extraction capability, reducing the number of parameters, and improving detection accuracy and efficiency. Finally, the proposed Focal-SIoU loss function replaces the original loss function, effectively reducing directional errors by combining angle, distance, shape, and IoU losses, thus optimizing the model training process. YOLOGX was evaluated on the D-Fire dataset, achieving a mAP@0.5 of 80.92% and a detection speed of 115 FPS, surpassing most existing classical detection algorithms and specialized fire detection models. These enhancements establish YOLOGX as a robust and efficient solution for forest fire detection, providing significant improvements in accuracy and reliability.