Abstract
Robot intelligent inspection is widely used in the positioning of various pointer instruments in power, petroleum, chemical, and other industries. Aiming at the technical problems of poor adaptability, poor real-time performance, and low positioning accuracy of the pointer instrument positioning method in the existing substation intelligent inspection robot system, we propose a simple and effective pointer instrument positioning detection algorithm. The algorithm first extracts locally adaptive regression kernels (LARK) features of the input image, and the dimension of the LARK feature is reduced using the principal components analysis algorithm. Then, the template image is slid in the input image, the cosine similarity is used as an evaluation index, and the Fourier transform is used to accelerate the convolution operation in the cosine similarity calculation. Finally, the accelerated-KAZE algorithm is used to extract the feature points of the pointer-type instrument area image and the template image, and the statistical method of grid motion was used to eliminate the wrong matching points. The remaining matching points were processed by random sample consensus algorithm, and the homography matrix was obtained. The image registration was completed by the homography matrix, and the pointer-type instrument region positioning was realized. The experimental results show that the proposed method has good adaptability, strong real-time performance, and high accuracy of pointer-type instrument positioning.
Highlights
There are a variety of instruments in the substation, and the textures of the various instruments are not consistent, so the direct use of the feature point matching algorithm will be affected by other environmental objects, resulting in positioning failure.[5]
The image registration technology effectively solves the positioning error caused by the angle of view deflection of the pointer-type instrument input image, improves the registration accuracy of the pointer-type instrument area positioning, and provides high-precision positioning for subsequent pointer-type meter reading recognition
Aiming at the positioning problem of pointer instrument in substation intelligent inspection robot system, the stable locally adaptive regression kernels (LARK) feature combined with principal components analysis (PCA) algorithm is used to highlight the edge feature of the instrument and improve the accuracy of the instrument positioning
Summary
For the target detection algorithm combined with classifier, due to the training of each classifier, a large number of samples are required, while in the substation specific scene, there are no various calibration samples These methods are not suitable for pointer instrument positioning in substation scenarios. Accurate positioning is the basis of pointer-type instrument recognition, and the difficulty of pointer-type instrument positioning is that the angle, time, and distance of shooting are different, and the illumination, position, posture, size, and color of the instrument in the picture will be biased.[4] In addition, there are a variety of instruments in the substation, and the textures of the various instruments are not consistent, so the direct use of the feature point matching algorithm will be affected by other environmental objects, resulting in positioning failure.[5]. The method based on grid motion statistics is used to eliminate the wrong matching points, and the secondary registration of the image is completed, which greatly improves the identification speed of pointer instrument, and effectively reduces the influence of illumination conditions, angle deflection and other factors on the identification accuracy of pointer instrument
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