Dual attention for power corridor point cloud semantic segmentation

doi:10.13474/j.cnki.11-2246.2025.0421

Abstract

Abstract: The point cloud scene of power corridors is unique and faces a number of challenges when using deep learning methods for semantic segmentation.There is a serious data skew problem in the scene, with background objects such as ground and vegetation occupying the majority, which may lead to degradation of network performance. In addition, when dealing with point cloud data of overhead power lines and power towers, the model has difficulty in extracting enough local features due to the insufficient number of points within the local radius, which reduces the segmentation accuracy of similar objects. For this reason, this paper designs a two-order semantic transmission line semantic segmentation method based on the dual attention mechanism. Firstly, in the data streamlining stage, the elevation difference between the power transmission equipment and the background is utilized to have remove a large number of background points, thus reducing the training burden of the subsequent neural network, accelerating the training process, and significantly alleviating the data skew problem. Then, the dual-attention model, which takes into account both global and local features, is proposed to enhance the differentiation of similar objects and improves the accuracy of point cloud segmentation. After testing, the data streamlining method in this paper can remove more than 63% of background points and partially solve the data skewing problem; the proposed dual-attention network outperforms other methods for segmentation of ground wires, conductors and insulators.

Key words: point cloud, power corridor, deep learning, dual attention, semantic segmentation

CLC Number:

P208

LI Jian, WANG Jian, WANG Lei, LI Min, YANG Like, ZHAO Yilong. Dual attention for power corridor point cloud semantic segmentation[J]. Bulletin of Surveying and Mapping, 2025, 0(4): 127-133.

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