mlsanityData to 14 Oct 2026

CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules

arXiv 2411.11011 ↗First posted 17 Nov 2024cs.CVcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 17 Nov 2024.

Builds on

17 of 37 references.

References · 17

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2020End-to-End Object Detection with Transformerscited by 7,414
  3. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  4. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  5. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
  6. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  7. 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 429
  8. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
  9. 2023RepViT: Revisiting Mobile CNN From ViT Perspectivecited by 123
  10. 2019CARAFE: Content-Aware ReAssembly of FEaturescited by 119
  11. 2018CGNet: A Light-weight Context Guided Network for Semantic Segmentationcited by 94
  12. 2023Learning to Upsample by Learning to Samplecited by 57
5 more
  1. 2024Poly Kernel Inception Network for Remote Sensing Detectioncited by 52
  2. 2023Rethinking Mobile Block for Efficient Attention-based Modelscited by 36
  3. 2022No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objectscited by 27
  4. 2024LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Networkcited by 27
  5. 2024Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseasescited by 15

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