FFAE-Net for automatic semantic segmentation of point clouds in the power corridor

It has significant application value by using LiDAR point clouds to analyze the safety of the power corridor. However, the current semantic segmentation network for point clouds has unsatisfactory feature extraction ability for typical targets (such as towers, power lines, and trees) in power corridors, and the segmentation accuracy is not high. In this regard, to address the unevenness of point clouds, the Fused Features and Attention Enhancement Network (FFAE-Net) is proposed in this paper, which mainly includes the proposed Contextual Feature Fusion (CFF) module and Neighbor Attention Pooling (NAP) module. In the CFF module, we integrate the geometric features of dense and sparse regions in the neighborhood, combined with the color change relationship, to deepen the network's understanding of point clouds unevenness and enable the network to fully learn effective information of the target. The NAP module introduces the attention mechanism, to enhance the aggregation for different features of point clouds, and improve the segmentation performance by weighting the main features of the targets in the power corridor. In addition, a new loss function is further constructed to address the unevenness problem of the point clouds, which introduces the geometric deviation between the neighborhood center points and standardized center points. Experiments on LiDAR point clouds of the power corridor indicate that FFAE-Net can achieve satisfactory segmentation results for the power corridor, and the mIoU for five categories of targets, namely, power lines, high-voltage towers, buildings, plants and the ground, can reach 93.78%, which is significantly better than several compared networks.