  Deep learning-based object detection is a powerful approach for detecting
faulty insulators in power lines. This involves training an object detection
model from scratch, or fine tuning a model that is pre-trained on benchmark
computer vision datasets. This approach works well with a large number of
insulator images, but can result in unreliable models in the low data regime.
The current literature mainly focuses on detecting the presence or absence of
insulator caps, which is a relatively easy detection task, and does not
consider detection of finer faults such as flashed and broken disks. In this
article, we formulate three object detection tasks for insulator and asset
inspection from aerial images, focusing on incipient faults in disks. We curate
a large reference dataset of insulator images that can be used to learn robust
features for detecting healthy and faulty insulators. We study the advantage of
using this dataset in the low target data regime by pre-training on the
reference dataset followed by fine-tuning on the target dataset. The results
suggest that object detection models can be used to detect faults in insulators
at a much incipient stage, and that transfer learning adds value depending on
the type of object detection model. We identify key factors that dictate
performance in the low data-regime and outline potential approaches to improve
the state-of-the-art.
