GRJointNET: Synergistic Completion and Part Segmentation on 3D Incomplete Point Clouds

Yigit, Gurses,; Taspinar, Melisa; Yurt, Mahmut; Özer, Sedat

doi:10.1109/siu53274.2021.9477918

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…To automatically extract the UAV inspection target points, the corresponding locations need to be found first, and then the segmentation is accomplished by using a deep learning point cloud segmentation algorithm with specific dataset training. In the field of deep learning, there are various public datasets adapted to different needs, among which the ShapeNet dataset [16] is widely used for testing part segmentation [17][18][19]. For research on using point cloud data for segmentation tasks, there are three main types of segmentation: projected image-based segmentation, voxel-based segmentation, and direct point-based segmentation [20,21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Target Point Localization for UAV Inspection of Point Cloud Transmission Towers

Li,

Chen

et al. 2024

Remote Sensing

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UAV transmission tower inspection is the use of UAV technology for regular inspection and troubleshooting of towers on transmission lines, which helps to improve the safety and reliability of transmission lines and ensures the stability of the power supply. From the traditional manual tower boarding to the current way of manually selecting target camera shooting points from 3D point clouds to plan the inspection path of the UAV, operational efficiency has drastically improved. However, indoor planning work is still labor-consuming and expensive. In this paper, a deep learning-based point cloud transmission tower segmentation (PCTTS) model combined with the corresponding target point localization algorithm is proposed for automatic segmentation of transmission tower point cloud data and automatically localizing the key inspection component as the target point for UAV inspection. First, we utilize octree sampling with unit ball normalization to simplify the data and ensure translation invariance before putting the data into the model. In the feature extraction stage, we encode the point set information and combine Euclidean distance and cosine similarity features to ensure rotational invariance. On this basis, we adopt multi-scale feature extraction, construct a local coordinate system, and introduce the offset-attention mechanism to enhance model performance further. Then, after the feature propagation module, gradual up-sampling is used to obtain the features of each point to complete the point cloud segmentation. Finally, combining the segmentation results with the target point localization algorithm completes the automatic extraction of UAV inspection target points. The method has been applied to six kinds of transmission tower point cloud data of part segmentation results and three kinds of transmission tower point cloud data of instance segmentation results. The experimental results show that the model achieves mIOU of 94.1% on the self-built part segmentation dataset and 86.9% on the self-built instance segmentation dataset, and the segmentation accuracy outperforms that of the methods for point cloud segmentation, such as PointNet++, DGCNN, Point Transformer, and PointMLP. Meanwhile, the experimental results of UAV inspection target point localization also verify the method’s effectiveness in this paper.

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