FasterGold-DETR: An Efficient End-to-End Fire Detection Model via Gather-and-Distribute Mechanism

Abstract

Fire detection technology based on deep learning methods has become a prevalent practice. However, the performance of current YOLO-based detection models is limited by NMS, and DETR-based detection models struggle with real-time performance. To address these challenges, a new fire detection model, FasterGold-DETR, is proposed. Firstly, this model introduces an innovative backbone network, FasterRepNet, which efficiently captures and retains feature information, thereby accelerating the model’s convergence speed. Secondly, we propose the AIFI-GD hybrid encoder to reduce information loss in intra-scale and cross-scale feature interactions and improve the ability to detect fire of different sizes. Furthermore, to adapt the complex fire scenarios, we extend the dataset based on the KMU Fire and Smoke database and replace the loss function with WIoU to enhance the model’s robustness. Experiments show that our proposed model outperforms mainstream object detection models in terms of accuracy and complexity.