GSF-YOLOv8: A Novel Approach for Fire Detection Using Gather-Distribute Mechanism and SimAM Attention

To address the current challenges in fire detection algorithms, including insufficient feature extraction, high computational complexity, limited deployment on resource-constrained devices, missed detections, false detections, and low accuracy, we developed a high-precision algorithm named GSF-YOLOv8. The GSF-YOLOv8 consists of three main parts: Firstly, we introduced the Gather-Distribute (GD) feature aggregation module, which enhances the recognition accuracy of fire features by fusing and extracting multi-scale information. Secondly, we integrated the SimAM attention module to optimize the collaborative learning of feature map dimensions and to reduce reliance on hyperparameter tuning through precise weight allocation, thereby improving the model’s rapid response capability to fires. Lastly, we proposed a Focal-DIoU Loss to replace the original loss function, optimizing bounding box regression and improving localization accuracy. It also focuses on hard samples to reduce the impact of easily misclassified samples, thereby enhancing the model’s performance in complex fire detection. We tested our method on the D-Fire dataset. The experimental results showed that GSF-YOLOv8 achieved higher mAP (80.65%) and faster detection speed (118 FPS). Meanwhile, the model’s complexity and number of parameters have been significantly optimized. The GSF-YOLOv8 not only significantly improves the efficiency and accuracy of fire detection but also provides a more reliable and accurate solution for real-time detection in similarly complex environments.