High-speed rail insulator inspection image identification method based on image library combination

Abstract

The invention discloses a high-speed rail insulator inspection image identification method based on image library combination. Taking an engineering actual measurement image in a high-speed rail contact network safety inspection system as an example, the method comprises the following steps: firstly, carrying out image enhancement on an ROI (Region of Interest) of an inspection image; The method comprises the steps of preprocessing such as image preprocessing, denoising and the like, then extracting positive and negative samples in a region of interest by adopting an image cutting mode, carrying out training learning on the positive and negative samples by utilizing a multi-layer CNN, and finally carrying out optimization processing such as hierarchical recognition, fine adjustment on an error set and the like on a trained model to realize accurate recognition on insulators in an inspection image. The method is especially suitable for the overhead line system environment with the complex background, the insulator can be accurately positioned in the overhead line system environment at the recognition rate of 98.2%, and the good generalization ability can be shown under different shooting distances, angles and brightness.

Description

technical field [0001] The patent of the present invention relates to the field of rail transit, in particular to a method for image recognition of high-speed rail insulator inspections combined with a library. Background technique [0002] With the rapid development and construction of my country's electrified railways, the requirements for the safety and reliability of the catenary power supply in the traction power supply system are constantly increasing. As one of the components on the catenary to keep live parts electrically insulated, insulators play a pivotal role in the safe operation of electrified railways. At present, the identification methods of insulators on the catenary mainly include manual inspection method and electromagnetic characteristic detection method. The manual inspection method has low recognition efficiency and long inspection period; electromagnetic characteristic detection methods such as voltage distribution method, ultrasonic detection, infrar...

AI Technical Summary

Problems solved by technology

Insulator image recognition technology can be mainly divided into the following three categories: the first category is based on the histogram of oriented gradient (HOG, Histogram of Oriented Gradient) features and support vector machine (SVM, SupportVector Machine) classifier for insulator recognition and location. Although the false recognition rate of the technology is low, the recognition accuracy is not high, and when the image background is complex or the number of insulators to be recognized is large, the detection accuracy will be greatly reduced, and it is difficult to meet the needs of actual engineering; the second type is based on The insulator feature recognition technology of Haar feature and Adaboost cascade classifier. Although this type of technology has high recognition accuracy, it has serious misidentification problems, and the misidentified objects are mainly black backgrounds in the image. By adding negative samples and It is also difficult to optimize the program to reduce the false recognition rate; the third type is a new type of detection technology that combines deep learning and machine vision. This type of technology has a wide range of applications and high detection efficiency. The recognition rate is still not high enough for insulator images with complex backgrounds

With the rapid development of my country's railways, the number of catenary insulators required to be detected has increased dramatically, and the above methods can no longer meet the processing needs of such massive image data.

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