Accurate Glass Insulators Defect Detection in Power Transmission Grids Using Aerial Image Augmentation

Abstract

Insulator is one of the most critical components of power transmission lines and its timely and accurate defect detection is considered important to ensure reliable and safe operation of transmission grids. This paper proposes an efficient augmentation method of aerial images captured using the unmanned aerial vehicle for the accurate detection of insulators with self-detonation defects. Through the adoption of the improved Resnet-18 model with the insulator edge features and the Grad-CAM based saliency map generation, the proposed solution can well maintain the vital regions for fine-grained classification in the augmentation process. The proposed solution is extensively assessed in comparison with the CNN-based benchmark methods through experiments. The numerical results indicate that the improved ResNet-18 model with the augmented images outperform the existing solutions and can identify the self-detonation defects with an accuracy of 95.1%.