AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION

This project aims to construct an autonomous power line inspection system using computer vision to classify and localise the normal and abnormal insulators. The traditional inspection methods, for instance, foot patrol inspection and helicopter-assisted inspection are time-consuming and dangerous. DenseNet-201 model is proposed as the base network to perform insulator fault detection autonomously. An algorithm with DenseNet-201 backbone consisting of two branches which are class label classification and bounding box regression is developed. The developed algorithm is trained on the augmented Chinese Power Line Insulator Dataset (CPLID) that consisted of normal and missing cap insulator images. The prediction results in classification accuracy of 100%. The average precision of detecting normal insulator and insulator missing cap has higher performance at threshold 0.3 which are 100% and 66.61%. The mean average precision at thresholds 0.3 and 0.5 are 83.31% and 64.62% respectively. The experimental results on the augmented CPLID dataset denote that the proposed model has high classification accuracy and it outperforms the ResNet model