Data Readiness and Data Exploration for Successful Power Line Inspection

Abstract

Sufficiently large, curated, and representative training data remains key to successful implementation of deep learning applications for wide-scale power line inspection. However, most researchers have offered limited insight regarding the inherent readiness of the knowledge bases that drives power line algorithm development. In most cases, these high dimensional datasets are also unexplored before modeling. In this article, power line image data readiness (PLIDaR) scale for AI algorithm development is proposed. Using the PLIDaR benchmark, this study analyzes the fundamental steps involved in preparing overhead transmission power line (OTPL) insulator image data for deep supervised learning algorithm development. Data visualization approach is implemented by reengineering the ground truth instance annotations of two recent public insulator datasets, while exploratory data analysis is also employed by implementing a robust dimensionality reduction technique to optimize construction, visualization, clustering, and analysis of these recent insulator datasets in a lower dimensional space. The implementations reveal representational variabilities and hidden patterns that could be exploited to improve data quality before predictive modeling. Moreover, the visualizations from dimensionality reduction technique have potential to help develop classifiers that are more reliable.