轻量版火焰检测模型，速度与精度兼优。
Light-YOLOv8-Flame: A Lightweight High-Performance Flame Detection Algorithm
- 用FasterNet块替代YOLOv8的C2f模块，降低计算开销。
- mAP提升0.78%，召回率增2.05%，参数减少25.34%。
- 适合部署在资源受限设备上的实时火焰检测。
基于计算机视觉的火灾检测算法面临计算成本高、响应延迟等问题，限制了其在实时系统中的应用。本文提出Light-YOLOv8-Flame，一种专为快速高效实时部署设计的轻量级火焰检测算法。通过将原YOLOv8架构中的C2f模块替换为FasterNet Block模块，该模块结合部分卷积（PConv）与常规卷积（Conv），有效降低计算复杂度和模型规模。使用包含7,431张图像的数据集（涵盖火焰与非火焰场景）进行训练与增强。实验表明，改进后的模型在mAP上提升0.78%，召回率提高2.05%，参数量减少25.34%，精度仅轻微下降0.82%。结果表明，Light-YOLOv8-Flame在检测性能与速度上均表现优异，适用于资源受限设备上的实时火焰检测。
原文摘要 · Abstract (English)
Fire detection algorithms, particularly those based on computer vision, encounter significant challenges such as high computational costs and delayed response times, which hinder their application in real-time systems. To address these limitations, this paper introduces Light-YOLOv8-Flame, a lightweight flame detection algorithm specifically designed for fast and efficient real-time deployment. The proposed model enhances the YOLOv8 architecture through the substitution of the original C2f module with the FasterNet Block module. This new block combines Partial Convolution (PConv) and Convolution (Conv) layers, reducing both computational complexity and model size. A dataset comprising 7,431 images, representing both flame and non-flame scenarios, was collected and augmented for training purposes. Experimental findings indicate that the modified YOLOv8 model achieves a 0.78% gain in mean average precision (mAP) and a 2.05% boost in recall, while reducing the parameter count by 25.34%, with only a marginal decrease in precision by 0.82%. These findings highlight that Light-YOLOv8-Flame offers enhanced detection performance and speed, making it well-suited for real-time fire detection on resource-constrained devices.
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