# Wildfire Smoke Detection System: Model Architecture, Training Mechanism, and Dataset

arXiv: https://arxiv.org/abs/2311.10116 · HTML: https://mlsanity.com/paper/2311.10116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications](https://mlsanity.com/paper/1905.11922/) (2019)
- [FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection](https://mlsanity.com/paper/2112.08598/) (2021)
- [Wildfire Detection Via Transfer Learning: A Survey](https://mlsanity.com/paper/2306.12276/) (2023)
- [Multimodal Wildland Fire Smoke Detection](https://mlsanity.com/paper/2212.14143/) (2022)

## Built on by (most cited first, 1 of 1)

- [A transformer boosted UNet for smoke segmentation in complex backgrounds in multispectral LandSat imagery](https://mlsanity.com/paper/2406.13105/) (2024)

## Abstract (arXiv)

Vanilla Transformers focus on semantic relevance between mid- to high-level features and are not good at extracting smoke features as they overlook subtle changes in low-level features like color, transparency, and texture which are essential for smoke recognition. To address this, we propose the Cross Contrast Patch Embedding (CCPE) module based on the Swin Transformer. This module leverages multi-scale spatial contrast information in both vertical and horizontal directions to enhance the network's discrimination of underlying details. By combining Cross Contrast with Transformer, we exploit the advantages of Transformer in global receptive field and context modeling while compensating for its inability to capture very low-level details, resulting in a more powerful backbone network tailored for smoke recognition tasks. Additionally, we introduce the Separable Negative Sampling Mechanism (SNSM) to address supervision signal confusion during training and release the SKLFS-WildFire Test dataset, the largest real-world wildfire testset to date, for systematic evaluation. Extensive testing and evaluation on the benchmark dataset FIgLib and the SKLFS-WildFire Test dataset show significant performance improvements of the proposed method over baseline detection models. The code and data are available at github.com/WCUSTC/CCPE.