基于深度学习的无人机输变配一体化巡检系统

Deep Learning-Based Unmanned Aerial Vehicle Integrated Inspection System for Power Transmission Transformation and Distribution

设计了一种基于深度学习的无人机输变配一体化巡检系统。该系统采用先进的计算机视觉算法,利用改进的YOLOv5网络实现了输变配设备缺陷的精确识别和定位。通过构建大规模的缺陷数据集并进行算法优化,系统显著提高了检测的准确率和可靠性。同时,系统集成了机载计算平台和地面监控软件等模块,实现了巡检数据的实时处理和远程监控。

This paper proposes a deep learning-based integrated inspection system for unmanned aerial vehicles(UAV)in power transmission,transformation,and distribution.The system utilizes advanced computer vision algorithms and an enhanced YOLOv5 network for the precise identification and positioning of defects in power transmission and distribution equipment.By establishing a large-scale defect dataset and optimizing the algorithm,the system significantly improves the accuracy and reliability of defect detection.Additionally,the system integrates modules such as an onboard computing platform and ground monitoring software,enabling real-time processing of inspection data and remote monitoring.