Design and Integration of Machine Learning-Based Vision System for Automated Power Line Inspection Using a Mobile Damping Robot

Abstract

Ensuring the structural integrity of overhead power line conductors is critical for maintaining the safety and reliability of the electrical grid. Environmental stressors such as moisture, dust, and wind-induced vibrations contribute to surface degradation, including corrosion and fretting, which compromise conductor conditions and therefore performance over time. This thesis presents a vision-based, autonomous inspection framework using a modified Mobile Damping Robot, with two distinct levels of health assessment. The first method focuses on a simpler binary classification, where image filtering techniques—such as Sobel, Scharr, and Gray-scale Variance Normalization—are compared to highlight defect patterns, followed by histogram-based feature extraction and classification into healthy or unhealthy categories using traditional supervised machine learning models, including Random Forest, Multi-Layer Perceptron, and Gradient Boosting. Next, the second method provides a more detailed assessment by classifying conductors into four categories: Healthy, Minor Corrosion, Pollution-Induced Corrosion, and Pollution-Induced Fretting. To facilitate this classification, real-world conductor images collected via the MDR were preprocessed using segmentation models, such as U-Net and the Segment Anything Model, to isolate the conductor from the background. Two deep learning models, a custom-designed Convolutional Neural Network and a ResNet-50 transfer learning model, were trained for multi-class classification. Experimental results validate the effectiveness of the ResNet-50 model, demonstrating the potential of vision-based inspection for enabling proactive, data-driven, and scalable power line maintenance. By automating condition assessment through image-based analysis, this approach facilitates early detection of degradation, reduces reliance on manual inspections, and supports enhanced operational planning across transmission infrastructure.