The automatic extraction of inspection points for pylons is essential for intelligent Unmanned Aerial Vehicle (UAV) power inspections, especially in generating inspection route. However, current researches primarily focus on power scene perception and power component fault detection, with little attention paid to the inspection point detection. The primary reason are the lack of public pylon datasets for keypoint detection and the neglect of distinguish the inspection points from the keypoints used in feature extraction. Regarding that, this paper proposes a two-stage unsupervised pylon keypoint detection (UPKD) method to improve the efficiency of power inspections. In the first stage, the UPKD Network processes the point cloud to generate candidate keypoints, which comprises two main components: a data normalization module and an unsupervised keypoint detection network (UKD-Net). The data normalization module compresses information based on the symmetric structure of pylons, thereby reducing instability in inspection point detection. The UKD-Net incorporates a Point Transformer layer that uses self-attention mechanisms to extract features from the point cloud. In the second stage, a convex optimization strategy is applied to filter and acquire inspection points. These inspection points are then interconnected using a shortest-path strategy to generate the UAV inspection route. Our dataset is obtained using the Riegl VUX-1 laser measurement system and comprises 3,296 pylons of 10 types. Each pylon’s point cloud contains up to 25,000 points, with a high point density of 100 pts/m2. Extensive experiments show that the UPKD Network achieved state-of-the-art performance on our dataset, with repeatability achieving 90.39%, effectiveness (the ratio of effective keypoints to annotation points) achieving 69.95%, and completeness (the ratio of detected annotation points to keypoints) achieving 79.55%.

中文翻译：

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UPKD：从 3D LiDAR 数据中进行无监督挂架关键点检测，用于自主无人机功率检查


塔架巡检点的自动提取对于智能无人机 （UAV） 电力巡检至关重要，尤其是在生成巡检路线时。然而，目前的研究主要集中在电力场景感知和电力组件故障检测上，而很少关注巡检点检测。主要原因是缺乏用于关键点检测的公共 pylon 数据集，并且忽视了区分检查点和特征提取中使用的关键点。对此，本文提出了一种两阶段无监督塔架关键点检测 （UPKD） 方法，以提高电力巡检的效率。在第一阶段，UPKD 网络处理点云以生成候选关键点，该关键点包括两个主要组件：数据归一化模块和无监督关键点检测网络 （UKD-Net）。数据归一化模块根据塔架的对称结构压缩信息，从而减少检测点检测的不稳定性。UKD-Net 包含一个 Point Transformer 层，该层使用自注意力机制从点云中提取特征。在第二阶段，应用凸优化策略来过滤和获取检查点。然后，这些检查点使用最短路径策略互连，以生成 UAV 检查路线。我们的数据集是使用 Riegl VUX-1 激光测量系统获得的，由 10 种类型的 3,296 个塔架组成。每个晶塔的点云最多包含 25,000 个点，点密度高达 100 pts/m2。广泛的实验表明，UPKD 网络在我们的数据集上实现了最先进的性能，重复性达到 90。39%，有效性 （有效关键点与注释点的比率） 达到 69.95%，完整性 （检测到的注释点与关键点的比率） 达到 79.55%。