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.
@article{arxiv.2411.11011,
title = {CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules},
author = {Kunwei Lv and Ruobing Wu and Suyang Chen and Ping Lan},
journal= {arXiv preprint arXiv:2411.11011},
year = {2025}
}