APE: Anomaly-Guided Progressively Balanced Ensemble Learning for Insulator Extraction From Imbalanced LiDAR Data

Extraction of insulators from power corridors is crucial for the construction of smart grids. Recent significant progress has been made in transmission line inspection through the application of uncrewed aerial vehicle light detection and ranging (UAV LiDAR) systems, which can efficiently capture 3-D structural information. However, accurately extracting insulator components from massive point clouds remains challenging due to their high similarity in shape with surrounding pylons and power lines, as well as the inherent data imbalance issues. After a series of preprocessing steps, numerous multiscale geometric features are designed and fused to precisely characterize insulators. Considering the issue of data imbalance due to the limited number of insulator samples, an anomaly-guided progressively balanced ensemble (APE) learning method is presented, using anomaly scores to achieve a good balance. Finally, the initial results are refined by incorporating spatial context information. The proposed delicate features and ensemble learning method were verified to be suitable for small insulator target extraction (including tangent insulators and tension insulators of different pylon types), achieving average precision (PR), recall (RE), and $F1$ -scores of up to 95%. The results can promote intelligent inspection and renovation of large-scale power corridors.