Fine-grained spatial-temporal perception for gas leak segmentation
This work addresses gas leak segmentation, a critical safety issue for environmental and human health, by introducing a novel method that improves accuracy, though it is incremental as it builds on existing segmentation techniques.
The paper tackles the problem of detecting and segmenting gas leaks, which are challenging due to their concealed appearance and random shapes, by proposing a Fine-grained Spatial-Temporal Perception (FGSTP) algorithm that integrates motion clues and refined object features, resulting in the most accurate mask compared to other state-of-the-art models on the manually labeled GasVid dataset.
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.