Overview
To improve the efficiency and accuracy of inspection for insulator strings, an autonomous UAV system is developed. The system autonomously recognizes the type of transmission tower and adaptively adjusts its flight path to effectively acquire high-quality insulator images. In addition, an anomaly detection model is proposed, and data imbalance is mitigated through synthetic anomaly generation, enabling reliable and autonomous inspection.
Key contribution
▸ Adaptive flight strategy for high-quality image acquisition: Flight paths are adaptively adjusted based on type of tower to acquire high-quality insulator images
▸ HAD-VAEC: Development of a deep neural network for effective detection of anomalies in insulator images
▸ Data imbalance mitigation method: Improvement of model performance by supplementing scarce fault data through synthetic anomaly generation
Impact
▸ Inspection capability based on autonomous flight: Establishment of an inspection framework applicable to diverse transmission environments
▸ Improved inspection quality and reliability: Consistent anomaly detection and decision support through analysis based on artificial intelligence
▸ Enhanced operational efficiency and cost efficiency: Reduced field workload and improved efficiency of inspection processes
▸ Foundation for scalable intelligent inspection systems: Expandable to various infrastructure and environments