  The widespread popularity of unmanned aerial vehicles enables an immense
amount of power lines inspection data to be collected. How to employ massive
inspection data especially the visible images to maintain the reliability,
safety, and sustainability of power transmission is a pressing issue. To date,
substantial works have been conducted on the analysis of power lines inspection
data. With the aim of providing a comprehensive overview for researchers who
are interested in developing a deep-learning-based analysis system for power
lines inspection data, this paper conducts a thorough review of the current
literature and identifies the challenges for future research. Following the
typical procedure of inspection data analysis, we categorize current works in
this area into component detection and fault diagnosis. For each aspect, the
techniques and methodologies adopted in the literature are summarized. Some
valuable information is also included such as data description and method
performance. Further, an in-depth discussion of existing deep-learning-related
analysis methods in power lines inspection is proposed. Finally, we conclude
the paper with several research trends for the future of this area, such as
data quality problems, small object detection, embedded application, and
evaluation baseline.
