DRC-YOLO: An Improved Fire Detection Algorithm Based on YOLO11

Abstract. Fire detection plays an important role in safety and loss reduction and is widely used in scenarios such as forests, industrial facilities and urban environments. However, fire detection faces many challenges, including the diversity of flame appearance, dynamic and unpredictable behaviour, and the complexity of distinguishing flames from similar visual phenomena. Exist-ing fire detection algorithms generally suffer from low detection accuracy, slow processing speed, and poor adaptability to complex backgrounds. To ad-dress these limitations, we propose a fire detection algorithm called DRC-YOLO, an enhanced model based on YOLO11. First, we replace some stand-ard convolution blocks with dynamic convolution layers, which improves the detection accuracy of irregular fire regions while maintaining the light-weight design of the model. Second, we integrated CBAM into the detection head and enhanced it through residual connections to further enhance the network's ability to localise fire-affected regions and improve robustness. Fi-nally, we enhanced the spatial pyramid structure by simulating large-kernel convolution operations, significantly expanding the model's receptive field while improving multi-scale feature extraction capability and maintaining computational efficiency. Extensive experiments on the M4SFWD dataset show that DRC-YOLO improves the AP by 2.6 , the AR by 2.2 and the mAP@50 by 1.8 , which are significant advantages over the baseline model.