Complex Scenes Fire Object Detection Based on Feature Fusion and Channel Attention

For recognizing small targets, fire-like objects in fire images, and detecting fires across various scenes, we propose a fire detection method based on feature fusion and channel attention. Most existing fire detection methods have specific application scenarios with poor speed or accuracy. To address the issues of poor accuracy when directly applying existing object detection models and the reduced detection speed when improving models for fire targets, our approach aims to balance accurate fire localization with real-time processing. In the backbone of the model, deformable convolution is used to capture rich image information, and channel attention is employed to enhance features. The feature fusion in the neck achieves better localization of small fire targets. The visualized heatmap results indicate the effectiveness of our improved measures. By simultaneously employing multiple improvement measures, our method achieved satisfactory fire detection performance. Experimental results on a self-annotated dataset demonstrate that the best AP@50 of the model can reach 63.9%, the fastest detection speed can reach 114 FPS, and the F1-score is stable at around 63%. Our method strikes a good balance between detection speed and accuracy.