Method for creating damage figure using the deep learning-based damage image classification of facility

摘要

The present invention relates to a damage level generation method using deep learning-based facility damage image classification, comprising the steps of: (a) augmenting the number of image data of a facility acquired by an image data augmentation module; (b) the supervised learning module learning the CNN by using the damaged image data and the undamaged image data that have undergone labeling to designate the presence or absence of damage for each class and pixel as training data; (c) automatically classifying the damaged image by using the damaged image classifier, which is a CNN in which supervised learning is completed, using test image data or arbitrary image data to obtain a degree of damage as input data; (d) pre-processing, by the damaged image data pre-processing module, the data classified as the damaged image as output data through the damaged image classifier to efficiently detect the damaged area; (e) detecting, by the damaged area detection module, the image damaged area in order to increase the accuracy of the degree of damage and extract the damage shape pattern for the damaged image on which the pre-processing operation has been completed, and (f) the damage level generating module for the damage By repeatedly using feature vectors and short line segments to automatically generate the damage level, deep learning technology was introduced to the detailed inspection of facilities, enabling objective, rapid and automatic damage recognition in a way that has been carried out for a long time centered on manpower. can make it