The Transformer-based deep networks have increasingly shown significant
advantages over CNNs. Some existing work has applied it in the field of
wildfire recognition or detection. However, we observed that the vanilla
Transformer is not friendly for extracting smoke features. Because low-level
information such as color, transparency and texture is very important for smoke
recognition, and transformer pays more attention to the semantic relevance
between middle- or high-level features, and is not sensitive to the subtle
changes of low-level features along the space. To solve this problem, we
propose the Cross Contrast Patch Embedding(CCPE) module based on the Swin
Transformer, which uses the multi-scales spatial frequency contrast information
in both vertical and horizontal directions to improve the discrimination of the
network on the underlying details. The fuzzy boundary of smoke makes the
positive and negative label assignment for instances in a dilemma, which is
another challenge for wildfires detection. To solve this problem, a Separable
Negative Sampling Mechanism(SNSM) is proposed. By using two different negative
instance sampling strategies on positive images and negative images
respectively, the problem of supervision signal confusion caused by label
diversity in the process of network training is alleviated. This paper also
releases the RealFire Test, the largest real wildfire test set so far, to
evaluate the proposed method and promote future research. It contains 50,535
images from 3,649 video clips. The proposed method has been extensively tested
and evaluated on RealFire Test dataset, and has a significant performance
improvement compared with the baseline detection models.

本研究提出了基于 Swin Transformer 的 Cross Contrast Patch Embedding (CCPE) 模块，用于提取烟雾特征，并引入了 Separable Negative Sampling Mechanism (SNSM) 解决了烟雾检测中的标签混淆问题。同时，通过发布迄今最大的真实火灾测试集 RealFire Test，对所提出的方法进行了广泛测试和评估，并与基线检测模型进行了显著性能改进的比较。