基于注意力模型的变电站场景语义分割

Qian Chen, Chang-Hua Zhang, Hao Li, Naijia Wan, Donghui Wang, Bin Xu
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引用次数: 0

摘要

巡检机器人广泛应用于变电站的巡检任务中。为了完成任务,机器人需要理解和识别道路场景。但是,变电站中存在大量相似的小型设备,难以识别。为了解决这个问题,我们提出了一个语义分割模型来识别道路场景。具体来说,我们提出的模型利用多视图方法来区分设备。我们使用注意机制来加强属于单个类别的像素之间的关系。与以往的方法相比,我们的方法在一个新的变电站数据集上取得了良好的性能,并且在变电站设备识别方面表现出鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Segmentation of Substation Scenes Using Attention-Based Model
Inspection robots are widely used in transformer substation for inspection tasks. In order to complete the task, the robots need to understand and recognize road scenes. However, the transformer substation contains lots of similar and small equipment, which are difficult to recognize. To address this problem, we propose a semantic segmentation model to recognize road scenes. Specifically, our proposed model discriminates the equipment by leveraging multi-view method. We use attention mechanism to strengthen the relationship between pixels that belong to an individual category. Compared with previous methods, our approach achieves a good performance in a new dataset from a transformer substation and shows robustness in recognizing equipment of transformer substation.
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