The smooth operation of power equipment is an important prerequisite for ensuring the production and life of residents. The insulator is an important component in the distribution network system. The defect size of the distribution network line insulator is small, and it is easy to be missing in the outdoor environment for a long time. The traditional target detection algorithm usually makes it difficult to identify the defect. Therefore, this paper proposes an insulator missing detection method based on improved YOLOv7 algorithm. Firstly, the insulator image data in the distribution network line is collected by the UAV; secondly, using the powerful feature extraction ability of the improved algorithm, the insulator is detected from the sample photo, and whether the insulator is normal or not is distinguished. Finally, the accuracy of the method is verified by experiments. The experimental results show that the accuracy of the method is 1.07 % higher than that of the original YOLOv7 algorithm, which can effectively distinguish whether the insulator is missing.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.