通过时空细粒度感知，精准分割隐蔽且形状不规则的气体泄漏
Fine-grained spatial-temporal perception for gas leak segmentation
- 利用帧间相关体积捕捉运动线索，结合逐级细化特征
- 在自建数据集GasVid上实现最精确的泄漏掩码分割
- 适合需要高精度动态非刚性目标分割的研究者
气体泄漏对人类健康和环境构成重大威胁。由于其隐蔽性和随机形状，现有方法难以高效准确地检测与分割。本文提出细粒度时空感知（FGSTP）算法用于气体泄漏分割。该方法在端到端网络中捕获连续帧间的运动线索，并融合精细的物体特征。首先构建相关体积以提取帧间运动信息；随后，通过先前输出逐步优化物体级特征；最后使用解码器精炼边界分割。由于缺乏高精度标注数据集，我们手动标注了气体泄漏视频数据集GasVid。在GasVid上的实验表明，该模型在分割非刚性物体（如气体泄漏）方面表现优异，生成的掩码精度优于其他先进模型。
原文摘要 · Abstract (English)
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.
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