Object detection in power line infrastructure: A review of the challenges and solutions

Abstract

Lack of proper maintenance of power line infrastructures is one of the main reasons behind power shortages and major blackouts. Current inspection methods are human-dependent, which is time-consuming and expensive. Recent progress in Unmanned Aerial Vehicles (UAVs) and digital cameras enforces the use of UAVs for power line inspection, reducing the cost and time to a great extent. Deep learning methods have recently proved their efficacy in the automatic analysis of power line data; however, they suffer from numerous challenges. Unlike generic object detection, power line inspection does not have large datasets. The data collection of power line objects is challenging compared to data collection for generic objects. As deep learning methods are data-hungry, difficulty in collecting training data raises class imbalance problems. Also, the real-time inspection of power line components demands compute-efficient deep learning methods, which is also challenging because of the high computational requirements of the generic deep learning-based object detectors. Despite being researched for decades, no object detectors can eliminate the effect of diverse challenges on the performance of deep learning methods. With these considerations, this study thoroughly reviews the existing works in the literature and the methods and approaches adopted in power line inspection to overcome these challenges. We also provide the type of faults addressed in the literature with details on the methods employed for their analysis. Finally, we conclude the review by providing insights into future research directions in power line inspection.