The invention discloses a method and device for monitoring the opening degree of a valve handle, a storage medium and electronic equipment, and relates to the technical field of intelligent safety detection. The method comprises the following steps: acquiring a current position frame including a valve in a current shot image; determining an image mask of a current valve handle in the current position frame; obtaining an angle difference between the characteristic axis of the image mask of the current valve handle and the characteristic axis of a standard image mask, wherein the standard imagemask is an image mask when the valve handle is in a closed state, which is determined according to shot images at the same position; and when the angle difference is larger than a preset angle, determining that the valve is not closed. By means of the method, whether the valve handle is closed or not can be determined in time, and the method can be suitable for open occasions.

Method and device for monitoring opening degree of valve handle

The invention provides a power distribution cabinet power failure and transmission state identification method and device based on machine vision, and relates to the technical field of power distribution cabinet monitoring, and the method comprises the steps: obtaining a target picture, and carrying out the preprocessing of the target picture; obtaining the area of a target contour based on the preprocessed target picture and judging whether the area of the target contour is within a preset value range; if the area of the target contour is within a preset value range, calculating a centroid position coordinate of the target contour based on the target contour and judging whether the centroid position coordinate is within the centroid position preset value range; if the center-of-mass position coordinate of the target contour is within the center-of-mass position preset value range, judging that the power distribution cabinet is in a power failure state; and if the center-of-mass position coordinate of the target contour is not in the center-of-mass position preset value range, determining that the power distribution cabinet is in a power transmission state. Through the method, theproblem of low intelligent degree of power distribution cabinet state recognition in the prior art is relieved, and the accuracy of power failure and transmission state recognition of the power distribution cabinet is improved.

Power distribution cabinet power failure and transmission state identification method and device based on machine vision

The invention discloses a reference-free spliced image quality evaluation method and system, and belongs to the technical field of image processing. The evaluation method comprises the following steps: firstly, calculating an adaptive threshold, and judging the detail complexity of a spliced image; then, determining the number of pixel points in the neighborhood of each pixel point according to the detail complexity of the spliced image, and acquiring a CLBP window gradient difference value of each pixel point through calculation; and then, acquiring the average CLBP window gradient differencevalue of the spliced image through calculation, so that the quality of the spliced image can be evaluated. According to the spliced image quality evaluation method provided by the invention, the quality of the spliced image is objectively evaluated by utilizing the average CLBP window gradient difference value (MCLBP _ M value) of the spliced image, the operation is simple, and an evaluation effect consistent with a subjective evaluation result can be achieved without reference images.

Reference-free spliced image quality evaluation method and system

The invention relates to a method for judging the closing operation of an isolating switch based on machine vision Combining depth learning with classic machine vision method, the accurate positioning and real-time analysis of knife-switch angle are realized Firstly, the knife-switch image is trained by depth learning, and the knife-switch initial positioning is realized by using the trained model, and the knife-switch state is obtained Extracting the shape, size and surrounding object features of the knife-switch image, and further determining the knife-switch position; Extracting edge lines of a local area where the knife and switch are located according to the knife and switch position, and obtaining all sets of parallel edge lines; Calculating the edge line direction and the foreground image direction, eliminating the interference edge line and obtaining the edge line set of the knife and switch; Using the edge line to calculate the opening and closing angle of the knife and switch, the real-time judgment of the opening and closing state of the knife and switch can be realized The method of the invention has the advantages of fast processing speed, high detection precisionand high practical application value

Machine vision-based method for judging the closing operation of disconnector

The invention relates to a noise detection method based on self-defined features of a single image, and the method comprises the steps: collecting a transformer substation image, and calculating an image x-direction gradient graph and an image y-direction gradient graph; traversing the to-be-detected image, and calculating a gradient direction angle of each pixel point according to the gradient map; giving a direction distribution number, calculating a direction distribution value of each pixel point in each direction according to the gradient value and the direction angle, and calculating anintegral graph so as to realize self-defined feature extraction; carrying out statistical analysis on the image gradient information and the self-defined features according to a self-defined feature extraction result, and obtaining an average gradient value and a main direction distribution value under given conditions; calculating an image noise degree, giving a noise degree threshold value, if the noise degree is greater than the given threshold value, considering that the image belongs to a noise image, and outputting the noise degree, otherwise, considering that the image belongs to a normal image, thereby realizing image noise detection. The method solves the problem of noise degree detection of a single image, and has important practical application value.

Zhang Nianyong

Papers

Machine vision-based method for judging the closing operation of disconnector

Noise detection method based on self-defined features of a single image