Intelligent Inspection of Transmission Lines Based on Multi-modal Deep Learning: UAV Image Recognition and Dynamic Fault Diagnosis Optimization

This paper proposes an intelligent inspection model for transmission lines based on multimodal deep learning, aiming to enhance the accuracy and efficiency of unmanned aerial vehicle (UAV) image recognition and achieve real-time diagnosis and optimization of dynamic faults in transmission lines. The system integrates various sensor information, including visible light image recognition, infrared thermal imaging, and LiDAR (Light Detection and Ranging) point cloud data, to construct a multimodal data fusion framework. This framework enhances the system’s ability to perceive the state of transmission lines in complex environments. This paper designs an improved deep learning model that, combined with lightweight edge computing technology, realizes high-precision recognition and classification of key components of transmission lines. In addition, the model incorporates a fault diagnosis algorithm based on Dynamic Time Warping (DTW) and an attention mechanism, enabling real-time state assessment of components such as insulator strings, fittings, and conductors in transmission lines, as well as prediction of potential fault risks. Experimental results demonstrate that the system significantly outperforms traditional methods in transmission line defect recognition tasks.