Analysis of Deep Learning Architectures and Efficacy of Detecting Forest Fires
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Abstract (arXiv)
The aim of this research is to review the state of computer vision as applied to combatting forest fires. My motivation to research this topic comes from the urgency with which new participants and stakeholders require guidance in this field. One of these new stakeholder groups are practitioners of machine learning that lack domain expertise. Introducing these new entrants to domain specific datasets and methods is critical to supporting this aim as general computer vision datasets are insufficient to support specialized research initiatives. The overarching aim of the research is to introduce datasets and methods to make them more accessible to the community.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 8 Dec 2022.
Builds on
9 of 33 references.
References · 9
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
- 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
- 2018Path Aggregation Network for Instance Segmentationcited by 796
- 2021ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biasescited by 484
- 2020Aerial Imagery Pile burn detection using Deep Learning: the FLAME datasetcited by 34
- 2015BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysiscited by 16
- 2021Is it enough to optimize CNN architectures on ImageNet?cited by 9
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