This paper proposes a new deep learning framework for the location of broken insulators(in particular the self-blast glass insulator)in aerial images.We address the broken insulators location problem in a low signal-n...This paper proposes a new deep learning framework for the location of broken insulators(in particular the self-blast glass insulator)in aerial images.We address the broken insulators location problem in a low signal-noise-ratio(SNR)setting.We deal with two modules:1)object detection based on Faster R-CNN,and 2)classification of pixels based on U-net.For the first time,our paper combines the above two modules.This combination is motivated as follows:Faster R-CNN is used to improve SNR,while the U-net is used for classification of pixels.A diverse aerial image set measured by a power grid in China is tested to validate the proposed approach.Furthermore,a comparison is made among different methods and the result shows that our approach is accurate in real time.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(No.61571296)the National Science Foundation of USA(No.CNS-1619250).
文摘This paper proposes a new deep learning framework for the location of broken insulators(in particular the self-blast glass insulator)in aerial images.We address the broken insulators location problem in a low signal-noise-ratio(SNR)setting.We deal with two modules:1)object detection based on Faster R-CNN,and 2)classification of pixels based on U-net.For the first time,our paper combines the above two modules.This combination is motivated as follows:Faster R-CNN is used to improve SNR,while the U-net is used for classification of pixels.A diverse aerial image set measured by a power grid in China is tested to validate the proposed approach.Furthermore,a comparison is made among different methods and the result shows that our approach is accurate in real time.
