mlsanityData to 7 Oct 2026

Benchmarking Multi-Scene Fire and Smoke Detection

arXiv 2410.16631 ↗First posted 22 Oct 2024cs.CVcited by 1

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Abstract (arXiv)

The current irregularities in existing public Fire and Smoke Detection (FSD) datasets have become a bottleneck in the advancement of FSD technology. Upon in-depth analysis, we identify the core issue as the lack of standardized dataset construction, uniform evaluation systems, and clear performance benchmarks. To address this issue and drive innovation in FSD technology, we systematically gather diverse resources from public sources to create a more comprehensive and refined FSD benchmark. Additionally, recognizing the inadequate coverage of existing dataset scenes, we strategically expand scenes, relabel, and standardize existing public FSD datasets to ensure accuracy and consistency. We aim to establish a standardized, realistic, unified, and efficient FSD research platform that mirrors real-life scenes closely. Through our efforts, we aim to provide robust support for the breakthrough and development of FSD technology. The project is available at hrefhttps://xiaoyihan6.github.io/FSD/https://xiaoyihan6.github.io/FSD/.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 22 Oct 2024.

Builds on

8 of 30 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  4. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  5. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  6. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  7. 2015BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysiscited by 16
  8. 2022Image-Based Fire Detection in Industrial Environments with YOLOv4cited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.