Transmission Line Quality Inspection Using AI

This paper presents the application of Artificial Intelligence (AI) techniques for quality inspection of transmission lines. Transmission lines play a crucial role in the reliable and efficient transfer of electrical power over long distances. However, faults and defects can occur due to various environmental and operational factors, leading to power outages and safety hazards. The proposed approach utilizes advanced Edge Deployable AI algorithms, including computer vision and Deep Learning, to automatically detect and classify potential faults and defects in transmission lines. The cutting edge high accuracy model is trained using i9 core, Nvidia RTX 3060 GPU with 6 GB vRAM, 16 GB RAM where we employ SSD - PyTorch based architecture and frameworks. The trained model is optimized to best accuracy and deployed over Nvidia Jetson Nano B01 using TensoRT Engine. A comprehensive dataset of images and videos of transmission lines is collected and labeled for training and testing the AI models. These models are trained to recognize patterns and anomalies indicative of various types of faults, such as broken conductors, insulator wear, and corrosion. The inspection process involves capturing images and videos of the transmission lines using drones or other surveillance systems. The captured data is then analyzed using the pre-trained AI models to identify potential issues. The AI algorithms can accurately detect and classify faults with high precision and recall, significantly reducing the need for manual inspection and minimizing human error. To enhance inspection accuracy, the AI models can be continuously updated and refined through a feedback loop using active learning algorithm.