The invention relates to an
aerial image insulator real-time detection method based on
deep learning. The
aerial image insulator real-time detection method based on
deep learning includes the steps: handing a task of extracting characteristics to a deep
convolutional neural network, extracting the deep characteristic information which is more comprehensive and can preferably describe an insulator,and inputting the deep characteristic information into a
detector to perform prediction
inference to obtain a detection result. For the
aerial image insulator real-time detection method based on deeplearning, the whole process is an end-to-end quick detection channel; a target frame is obtained after the image is input; the efficiency of subsequent automatic fault diagnosis is improved; and theaerial image insulator real-time detection method based on
deep learning is conductive to reducing the retrieval pressure and intensity when the line patrol staff retrieves the
mass line patrol data at present. And at the same time, the aerial image insulator real-time detection method based on deep learning also utilizes the idea of transfer learning to transfer the knowledge obtained from the past task to the current target task, so as to enable the trained model to have inheritability; whenever new data is added into an image
library, the target model can continue to
train new data on the basis of a
source model, so as to quickly achieve the expected effect and enable the old version of model not to be of no use at all because of updating of data; and the detection model can become moreand more powerful following increase of data as time goes on.