Abstract
With the wide application of infrared image acquisition technology in power inspection, a large number of infrared images of power equipment have been obtained. The traditional machine learning method has low accuracy and poor generalization. Therefore, in this paper, the deep learning technology is applied to infrared image detection of power equipment, and a defect detection method based on Faster region convolution neural network (RCNN) is proposed. In this method, the deep residual network is used to extract image features, and the regional proposal network is optimized according to the shape characteristics of power equipment, and the network is trained with the help of shared convolution layer. The experimental results show that the proposed method has high detection accuracy, good robustness and generalization ability.
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 callDisclaimer: 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.
