CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules
This work addresses fire detection for safety applications, but it is incremental as it builds on an existing YOLOv8 model with specific modifications.
The paper tackled the problem of detecting small fires and smoke in urban and forested areas by enhancing the YOLOv8 model with CARAFE and context-guided modules, resulting in improved detection precision compared to YOLOv8n.
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.