Lu Junjie, Zheng Yuchen, Guan Liwei, Lin Bing, Shi Wenzao, Zhang Junyan, Wu Yunping
College of Photonic and Electronic Engineering, Fujian Normal University, Fujian, China.
College of Physics and Energy, Fujian Normal University, Fujian, China.
PLoS One. 2025 Aug 7;20(8):e0329555. doi: 10.1371/journal.pone.0329555. eCollection 2025.
The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. Secondly, the Cross-Scale Feature Fusion Module (CCFM) and the Mixed Local Channel Attention (MLCA) mechanism are incorporated into the neck network to improve detection performance for small fire targets and reduce resource consumption. Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. Experimental results on a custom fire dataset demonstrate that FCMI-YOLO increases mAP@50 by 1.5%, reduces parameters by 40%, and lowers GFLOPs to 28.9% of YOLOv5s, demonstrating its practical value for real-time fire detection in edge scenarios with limited computational resources. The core code and dataset are available at https://github.com/ JunJieLu20230823/code.git.
物联网（IoT）技术和深度学习的快速发展推动了基于视觉的火灾检测算法在边缘设备上的部署，在硬件资源受限的情况下，显著加剧了准确性和推理速度之间的权衡。为了解决这个问题，本文提出了FCMI-YOLO，一种针对边缘设备优化的实时火灾检测算法。首先，提出了FasterNext模块，通过轻量化设计降低计算成本并提高检测精度。其次，将跨尺度特征融合模块（CCFM）和混合局部通道注意力（MLCA）机制纳入颈部网络，以提高对小火目标的检测性能并减少资源消耗。最后，提出了内交并比损失函数来优化边界框回归。在自定义火灾数据集上的实验结果表明，FCMI-YOLO将mAP@50提高了1.5%，参数减少了40%，GFLOPs降低到YOLOv5s的28.9%，证明了其在计算资源有限的边缘场景中进行实时火灾检测的实用价值。核心代码和数据集可在https://github.com/ JunJieLu20230823/code.git获取。
