YOLO-FireAD通过注意力引导残差结构和双池化融合，提升火灾检测精度与效率。
YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservation
- 引入注意力引导的倒置残差模块，自适应增强火源特征
- 双池化下采样融合保留多尺度火情特征，减少小火漏检
- 参数量降低51.8%，在多个数据集上精度优于主流实时模型
动态环境中的火灾检测面临光照变化干扰、误报漏报频发等问题，难以兼顾效率与准确率。针对现有基于YOLO模型的特征提取局限与信息丢失问题，本文提出面向火灾检测的YOLO-FireAD模型，包含两项核心创新：(1) 注意力引导的倒置残差块（AIR）结合通道-空间混合注意力与倒置残差结构，自适应增强火源特征并抑制环境噪声；(2) 双池化下采样融合块（DPDF）通过可学习融合最大值与平均值池化输出，有效保留多尺度火情模式，缓解小火检测失败问题。在两个公开数据集上的大量实验表明，本模型参数总量仅为1.45M（比YOLOv8n低51.8%），计算量4.6G（比YOLOv8n低43.2%），且mAP75高于主流实时目标检测模型YOLOv8n、YOLOv9t、YOLOv10n、YOLO11n、YOLOv12n及其它YOLOv8变体1.3%-5.5%。
原文摘要 · Abstract (English)
Fire detection in dynamic environments faces continuous challenges, including the interference of illumination changes, many false detections or missed detections, and it is difficult to achieve both efficiency and accuracy. To address the problem of feature extraction limitation and information loss in the existing YOLO-based models, this study propose You Only Look Once for Fire Detection with Attention-guided Inverted Residual and Dual-pooling Downscale Fusion (YOLO-FireAD) with two core innovations: (1) Attention-guided Inverted Residual Block (AIR) integrates hybrid channel-spatial attention with inverted residuals to adaptively enhance fire features and suppress environmental noise; (2) Dual Pool Downscale Fusion Block (DPDF) preserves multi-scale fire patterns through learnable fusion of max-average pooling outputs, mitigating small-fire detection failures. Extensive evaluation on two public datasets shows the efficient performance of our model. Our proposed model keeps the sum amount of parameters (1.45M, 51.8% lower than YOLOv8n) (4.6G, 43.2% lower than YOLOv8n), and mAP75 is higher than the mainstream real-time object detection models YOLOv8n, YOL-Ov9t, YOLOv10n, YOLO11n, YOLOv12n and other YOLOv8 variants 1.3-5.5%. For more details, please visit our repository: https://github.com/JEFfersusu/YOLO-FireAD
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