改进YOLOv8检测小火和烟雾，提升城市与森林火灾识别能力
CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules
- 引入CARAFE上采样和上下文引导模块，减少特征丢失
- 在真实场景数据集上精度显著高于原版YOLOv8n
- 适合需要高精度火情监测的安防与林业应用
城市与林区的火灾事件带来严重威胁，亟需更有效的检测技术。为此，本文提出CCi-YOLOv8n，一种针对小火及烟雾检测优化的YOLOv8模型。该模型融合CARAFE上采样算子与上下文引导模块，缓解上采样与下采样过程中的信息损失，保留更丰富的特征表示；同时采用增强型倒置残差移动块的C2f模块，有效捕捉小目标与细微烟雾纹理，显著提升原模型的检测能力。为验证性能，我们构建了Web-Fire数据集，涵盖多样真实场景下的火情与烟雾样本。实验结果表明，CCi-YOLOv8n在检测精度上优于YOLOv8n，证实其在复杂环境下具备鲁棒性火情检测能力。
原文摘要 · Abstract (English)
Fire incidents in urban and forested areas pose serious threats,underscoring the need for more effective detection technologies. To address these challenges, we present CCi-YOLOv8n, an enhanced YOLOv8 model with targeted improvements for detecting small fires and smoke. The model integrates the CARAFE up-sampling operator and a context-guided module to reduce information loss during up-sampling and down-sampling, thereby retaining richer feature representations. Additionally, an inverted residual mobile block enhanced C2f module captures small targets and fine smoke patterns, a critical improvement over the original model's detection capacity.For validation, we introduce Web-Fire, a dataset curated for fire and smoke detection across diverse real-world scenarios. Experimental results indicate that CCi-YOLOv8n outperforms YOLOv8n in detection precision, confirming its effectiveness for robust fire detection tasks.
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