Abstract
Deep learning classifies information and combines the underlying features to form more abstract high-level features. Its ability of autonomous learning also provides favorable conditions for industrial application. Aiming at the problem of insufficient performance of current small target detection, this paper proposes a deep learning algorithm using One-stage fusion two-stage. The influence of background noise on network training is reduced. The algorithm combines multi-scale training methods and achieves sample equilibrium adjustment based on sample distribution and stratified sampling. The high efficiency of YOLOv3 method for large target detection is well used, and the multi-scale training advantages of SNIPER algorithm are also played out, which makes the intelligent detection effect of pin defect of transmission line get some performance improvement.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Earth and Environmental Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
