Abstract: A heterogeneous image registration method includes: performing edge detection on collected images, in combination with a curvature scale space strategy to extract contour curved segments in an edge image. Implementing a feature point detection strategy based on global and local curvature detecting feature points in the contour curved segments, and obtaining the nearest minimum local curvature of the feature points pointing to starting and end points of the contour, respectively. Calculating the number of neighborhood sampling points and neighborhood auxiliary feature points of neighborhoods on both edges of each of the feature points according to the nearest minimum local curvature. Using neighborhood auxiliary feature points and feature points to form a feature triangle, calculating an angle bisector vector and a main direction corresponding to the feature point in the feature triangle.

Abstract: The present invention provides a high-precision identification method and system for substations, including building a Mask RCNN objection recognition network model based on convolutional neural networks; inputting acquired image information of a object into the Mask RCNN object recognition network model for preliminary recognition and outputting a recognition result of the object; using an information entropy to create a semantic decision tree and correcting the recognition result of the object according to a principle of relative correlation between different objects and outputting a final recognition decision result; reading the recognition decision result to obtain a true type of the object to be recognized. The present invention greatly improves the accuracy of image recognition of substations, and has a positive role in the research and development of automatic inspection equipment for inspection robots.

Abstract: The present disclosure provide a method for identifying power equipment targets based on human-level concept learning, including: creating a dataset of power equipment images, and annotating power equipment in power equipment images; training neural network and Bayesian network with the annotated dataset and respectively acquire identification results and conditional probabilities; calculating probabilities of unions with the conditional probabilities; and filtering the identification result corresponding to the highest probability of the union as identification result of the dataset of the power equipment images and complete the identification of the power equipment. The present disclosure combines Mask R-CNN and probabilistic graphical model. The bottom layer uses Mask R-CNN, and the top layer uses Bayesian network to train in identifying power equipment images, so that a small amount of data samples can achieve good recognition, which improved the performance of Mask R-CNN model.

Abstract: The present invention provides a high-precision identification method and system for substations, including building a Mask RCNN objection recognition network model based on convolutional neural networks; inputting acquired image information of a object into the Mask RCNN object recognition network model for preliminary recognition and outputting a recognition result of the object; using an information entropy to create a semantic decision tree and correcting the recognition result of the object according to a principle of relative correlation between different objects and outputting a final recognition decision result; reading the recognition decision result to obtain a true type of the object to be recognized.

Abstract: A heterogeneous image registration method includes: performing edge detection on collected images, in combination with a curvature scale space strategy to extract contour curved segments in an edge image. Implementing a feature point detection strategy based on global and local curvature detecting feature points in the contour curved segments, and obtaining the nearest minimum local curvature of the feature points pointing to starting and end points of the contour, respectively. Calculating the number of neighborhood sampling points and neighborhood auxiliary feature points of neighborhoods on both edges of each of the feature points according to the nearest minimum local curvature. Using neighborhood auxiliary feature points and feature points to form a feature triangle, calculating an angle bisector vector and a main direction corresponding to the feature point in the feature triangle.

Abstract: The present disclosure provide a method for identifying power equipment targets based on human-level concept learning, including: creating a dataset of power equipment images, and annotating power equipment in power equipment images; training neural network and Bayesian network with the annotated dataset and respectively acquire identification results and conditional probabilities; calculating probabilities of unions with the conditional probabilities; and filtering the identification result corresponding to the highest probability of the union as identification result of the dataset of the power equipment images and complete the identification of the power equipment. The present disclosure combines Mask R-CNN and probabilistic graphical model. The bottom layer uses Mask R-CNN, and the top layer uses Bayesian network to train in identifying power equipment images, so that a small amount of data samples can achieve good recognition, which improved the performance of Mask R-CNN model.