CN2VF-Net: A Hybrid Convolutional Neural Network and Vision Transformer Framework for Multi-Scale Fire Detection in Complex Environments

Fire detection remains a challenging task due to varying fire scales, occlusions, and complex environmental conditions. This paper proposes the CN2VF-Net model, a novel hybrid architecture that combines vision Transformers (ViTs) and convolutional neural networks (CNNs), effectively addressing these challenges. By leveraging the global context understanding of ViTs and the local feature extraction capabilities of CNNs, the model learns a multi-scale attention mechanism that dynamically focuses on fire regions at different scales, thereby improving accuracy and robustness. The evaluation on the D-Fire dataset demonstrate that the proposed model achieves a mean average precision at an IoU threshold of 0.5 (mAP50) of 76.1%, an F1-score of 81.5%, a recall of 82.8%, a precision of 83.3%, and a mean IoU (mIoU50–95) of 77.1%. These results outperform existing methods by 1.6% in precision, 0.3% in recall, and 3.4% in F1-score. Furthermore, visualizations such as Grad-CAM heatmaps and prediction overlays provide insight into the model’s decision-making process, validating its capability to effectively detect and segment fire regions. These findings underscore the effectiveness of the proposed hybrid architecture and its applicability in real-world fire detection and monitoring systems. With its superior performance and interpretability, the CN2VF-Net architecture sets a new benchmark in fire detection and segmentation, offering a reliable approach to protecting life, property, and the environment.