CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913925410324480 |
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| author | Lv, Kunwei Wu, Ruobing Chen, Suyang Lan, Ping |
| author_facet | Lv, Kunwei Wu, Ruobing Chen, Suyang Lan, Ping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11011 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules Lv, Kunwei Wu, Ruobing Chen, Suyang Lan, Ping Computer Vision and Pattern Recognition 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. |
| title | CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.11011 |