Machine vision-based fabric physical property detection method and device

Abstract

The invention discloses a machine vision-based fabric physical property detection method and device. The machine vision-based fabric physical property detection method comprises the following steps: a, arranging an area-array camera with a telecentric lens and an LED (light-emitting diode) light source in a face-to-face manner, and forming a detection area between the area-array camera and the LED light source; b, controlling the area-array camera by a computer; c, enabling a fabric to be detected to pass through the detection area and the LED light source to irradiate the fabric to be detected, and photographing the fabric to be detected by the area-array camera; and d, transmitting images photographed by the area-array camera to the computer, converting the images of the fabric to be detected from time domain images to frequency domain images by the computer, extracting peak position coordinates of the horizontal direction and / or vertical direction of center position coordinates from the frequency domain images, calculating the distance between the peak position coordinates and the center position coordinates, and then calculating the density value of the fabric to be detected and / or the weft bevel angle of the fabric to be detected. The machine vision-based fabric physical property detection device comprises a framework, an LED light source and an area-array camera, wherein a telecentric lens is mounted on the area-array camera. According to the machine vision-based fabric physical property detection method and device, the images of a fabric flowing on the production line can be acquired, and high detection accuracy is achieved.

Description

technical field [0001] The invention relates to a textile testing method and equipment, in particular to a testing method and device for testing physical properties of fabrics. Background technique [0002] The detection of physical properties such as fabric density and weft angle is one of the most important links in textile quality testing. Accurate control of fabric density or number of stitches can ensure the fabric's setting effect and stability of shrinkage. Human eye measurement has the problems of high labor intensity, high error rate, and low efficiency. It is affected by subjective factors and needs to be improved urgently. [0003] In recent years, there are mainly three methods for fabric density detection on the market. One is to use laser scanning detection. The laser scanner generates a beam of light and is reflected by the polygonal mirror wheel, so that the entire width of the fabric to be inspected (except for the selvage) is produced. The light is scanned...

AI Technical Summary

Problems solved by technology

However, this method must replace different light sources during detection, which is not convenient for detecting fabrics with different texture structures, especially some thick fabrics, the detection accuracy is low, and the detection versatility of fabric density detection equipment is not strong

The third is the machine vision density detection using ordinary CCD area array cameras. Most of the existing products are used to detect static fabrics. For each detection, the brightness of the light source and the focal length of the camera need to be adjusted to obtain a clear image of the fabric. The operation is troublesome. External factors can easily lead to unclear images, making the detection accuracy of fabric density not high, which cannot meet the needs of industrial production

The disadvantages of this method are: 1. After converting to the frequency domain, filtering operation is required, and then the fabric image is reconstructed into a single weft yarn image, which increases the computing time of real-time detection; 2. Using the adaptive local threshold method Determined thresholds are not reliable for localized yarn stability

When the above fabric density detection equipment is in use, the light source and camera or camera lens irradiate and take pictures of the fabric (cloth) laid on a flat table. This structure is not convenient for detecting fabrics with different texture structures, making the fabric density The detection versatility of the detection equipment is not strong; and each detection needs to adjust the brightness of the light source and the focal length of the camera to obtain a clear image of the fabric, and it is very troublesome to adjust the brightness of the light source and the focal length of the camera, and the influence of external factors can easily lead to The image is unclear, so that the detection accuracy of the fabric density is not high; the fabric density detection equipment of the prior art can only collect images of static fabrics laid on a flat table, and cannot be used to collect images of fabrics flowing on the production line, which cannot satisfy The need for industrial production

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