FoSp: Focus and Separation Network for Early Smoke Segmentation
Lujian Yao Haitao Zhao Jingchao Peng Zhongze Wang Kaijie Zhao
Early smoke segmentation (ESS) enables the accurate identification of smoke
sources, facilitating the prompt extinguishing of fires and preventing
large-scale gas leaks. But ESS poses greater challenges than conventional
object and regular smoke segmentation due to its small scale and transparent
appearance, which can result in high miss detection rate and low precision. To
address these issues, a Focus and Separation Network (FoSp) is proposed. We
first introduce a Focus module employing bidirectional cascade which guides
low-resolution and high-resolution features towards mid-resolution to locate
and determine the scope of smoke, reducing the miss detection rate. Next, we
propose a Separation module that separates smoke images into a pure smoke
foreground and a smoke-free background, enhancing the contrast between smoke
and background fundamentally, improving segmentation precision. Finally, a
Domain Fusion module is developed to integrate the distinctive features of the
two modules which can balance recall and precision to achieve high F_beta.
Futhermore, to promote the development of ESS, we introduce a high-quality
real-world dataset called SmokeSeg, which contains more small and transparent
smoke than the existing datasets. Experimental results show that our model
achieves the best performance on three available datasets: SYN70K (mIoU:
83.00%), SMOKE5K (F_beta: 81.6%) and SmokeSeg (F_beta: 72.05%). Especially, our
FoSp outperforms SegFormer by 7.71% (F_beta) for early smoke segmentation on
SmokeSeg.