Measurement method of pillow spring based on machine vision

Abstract

In the current maintenance of pillow springs of railway freight cars in our country, the measuring method of manually using measuring scale and measure gauges is still common. In order to improve the quality and efficiency of the pillow spring measurement, and to support the automatic upgrade of the pillow spring maintenance line, a new method for measuring the size of the pillow spring based on machine vision is proposed. By analyzing the requirements and characteristics of pillow spring detection and matching, a visual measurement system was designed to replace manual measurement, and openCV was used for image processing, feature extraction and size calibration. Compared with manual measurement, the measurement accuracy and efficiency of this method are higher. Experiments were conducted under different lighting conditions, and the measurement results have good repeatability and stability, indicating that the method has good robustness to the light intensity of the pillow spring environment.