Power Line Aerial Image Restoration under dverse Weather: Datasets and Baselines
Sai Yang, Bin Hu, Bojun Zhou, Fan Liu, Xiaoxin Wu, Xinsong Zhang,, Juping Gu, Jun Zhou

TL;DR
This paper introduces a new task called Power Line Aerial Image Restoration under Adverse Weather, providing datasets and baseline methods to improve image quality and detection accuracy in power line inspections affected by weather conditions.
Contribution
It is the first to release comprehensive datasets for power line aerial image restoration under various adverse weather conditions and evaluates baseline restoration methods on these datasets.
Findings
Baseline methods improve image quality under bad weather conditions.
Datasets enable benchmarking of restoration techniques for power line imagery.
Restoration enhances detection accuracy in power line autonomous inspections.
Abstract
Power Line Autonomous Inspection (PLAI) plays a crucial role in the construction of smart grids due to its great advantages of low cost, high efficiency, and safe operation. PLAI is completed by accurately detecting the electrical components and defects in the aerial images captured by Unmanned Aerial Vehicles (UAVs). However, the visible quality of aerial images is inevitably degraded by adverse weather like haze, rain, or snow, which are found to drastically decrease the detection accuracy in our research. To circumvent this problem, we propose a new task of Power Line Aerial Image Restoration under Adverse Weather (PLAIR-AW), which aims to recover clean and high-quality images from degraded images with bad weather thus improving detection performance for PLAI. In this context, we are the first to release numerous corresponding datasets, namely, HazeCPLID, HazeTTPLA, HazeInsPLAD for…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage · Power Line Inspection Robots
