轻量级火焰检测模型EFA-YOLO，速度快精度高，适合实时安防场景。
EFA-YOLO: An Efficient Feature Attention Model for Fire and Flame Detection
- 融合注意力机制与深度可分离卷积，提升特征提取效率。
- 参数仅140万，推理速度达22.19毫秒/图，比主流模型快88倍。
- 专为低资源设备设计，适合智能城市火灾预警系统部署。
作为突发性强、破坏力大的自然灾害，火灾长期威胁人类社会与生态环境。近年来，随着智慧城市与物联网技术的快速发展，基于深度学习的火灾检测系统逐渐成为应对火灾隐患的关键手段。然而，现有模型在复杂场景下仍面临检测精度与实时性不足的问题。为此，本文提出两个核心模块：EAConv（高效注意力卷积）与EADown（高效注意力下采样）。EAConv通过结合高效注意力机制与深度可分离卷积，显著提升特征提取效率；EADown则融合空间与通道注意力机制及池化操作，增强特征下采样的准确性与效率。基于此，我们设计了轻量级火焰检测模型EFA-YOLO。实验结果表明，EFA-YOLO模型参数量仅为1.4M，GFLOPs为4.6，CPU单图推理时间仅22.19ms。相比主流模型（如YOLOv5、YOLOv8、YOLOv9、YOLOv10），其在检测精度（mAP）和推理速度上均有显著提升，参数量减少94.6%，推理速度提升88倍。
原文摘要 · Abstract (English)
As a natural disaster with high suddenness and great destructiveness, fire has long posed a major threat to human society and ecological environment. In recent years, with the rapid development of smart city and Internet of Things (IoT) technologies, fire detection systems based on deep learning have gradually become a key means to cope with fire hazards. However, existing fire detection models still have many challenges in terms of detection accuracy and real-time performance in complex contexts. To address these issues, we propose two key modules: EAConv (Efficient Attention Convolution) and EADown (Efficient Attention Downsampling). The EAConv module significantly improves the feature extraction efficiency by combining an efficient attention mechanism with depth-separable convolution, while the EADown module enhances the accuracy and efficiency of feature downsampling by utilizing spatial and channel attention mechanisms in combination with pooling operations. Based on these two modules, we design an efficient and lightweight flame detection model, EFA-YOLO (Efficient Feature Attention YOLO). Experimental results show that EFA-YOLO has a model parameter quantity of only 1.4M, GFLOPs of 4.6, and the inference time per image on the CPU is only 22.19 ms. Compared with existing mainstream models (e.g., YOLOv5, YOLOv8, YOLOv9, and YOLOv10), EFA-YOLO exhibits a significant enhancement in detection accuracy (mAP) and inference speed, with model parameter amount is reduced by 94.6 and the inference speed is improved by 88 times.
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