Search

We consider typical environmental conditions, such as different shooting backgrounds, lighting, and shooting distances, and study adaptive shooting algorithms for unmanned aerial vehicle (UAV) on-site environments under typical working conditions to achieve high-quality acquisition of UAV inspection images and reduce the technical difficulty of artificial intelligence recognition of inspection images. Based on the collected high-quality images and the characteristics of unmanned aerial vehicle inspection images, a deep learning technology framework is adopted to study the intelligent identification method of hidden dangers in transmission line equipment and channel environment, achieving the intelligent recognition of pin level fine particle defects. [ABSTRACT FROM AUTHOR]