Gas leaks pose significant risks to human health and the environment. Despite long-standing concerns, there are limited methods that can efficiently and accurately detect and segment leaks due to their concealed appearance and random shapes. In this paper, we propose a Fine-grained Spatial-Temporal Perception (FGSTP) algorithm for gas leak segmentation. FGSTP captures critical motion clues across frames and integrates them with refined object features in an end-to-end network. Specifically, we first construct a correlation volume to capture motion information between consecutive frames. Then, the fine-grained perception progressively refines the object-level features using previous outputs. Finally, a decoder is employed to optimize boundary segmentation. Because there is no highly precise labeled dataset for gas leak segmentation, we manually label a gas leak video dataset, GasVid. Experimental results on GasVid demonstrate that our model excels in segmenting non-rigid objects such as gas leaks, generating the most accurate mask compared to other state-of-the-art (SOTA) models.

翻译：气体泄漏对人类健康和环境构成重大风险。尽管长期以来备受关注，但由于泄漏外观隐蔽且形状随机，能够高效准确检测和分割泄漏的方法十分有限。本文提出一种用于气体泄漏分割的细粒度时空感知（FGSTP）算法。FGSTP通过端到端网络捕捉跨帧的关键运动线索，并将其与精细化目标特征相融合。具体而言，我们首先构建相关性体积以捕获连续帧间的运动信息；随后，细粒度感知模块利用先前的输出逐步优化目标级特征；最后，采用解码器优化边界分割。由于目前缺乏高精度标注的气体泄漏分割数据集，我们手动标注了气体泄漏视频数据集GasVid。在GasVid上的实验结果表明，相较于其他最先进（SOTA）模型，我们的模型在分割气体泄漏等非刚性物体方面表现优异，能够生成最精确的掩码。