Sealing ring surface defect detection method based on machine vision

A technology of defect detection and machine vision, applied in the direction of optical defect/defect, instrument, measuring device, etc., to achieve the effect of high accuracy and strong algorithm robustness

Inactive Publication Date: 2016-07-20
NANJING UNIV OF SCI & TECH
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Problems solved by technology

The method based on shape and geometric feature extraction (including area, length, etc.) can effectively classify images, so as to determine whether the measured object has defe

Method used

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  • Sealing ring surface defect detection method based on machine vision
  • Sealing ring surface defect detection method based on machine vision
  • Sealing ring surface defect detection method based on machine vision

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Embodiment 1

[0039] to combine Figure 1 to Figure 6 , a method for detecting surface defects of sealing rings based on machine vision, the method steps are as follows:

[0040] Step 1. Use the camera to collect the surface image of the sealing ring, and perform adaptive median filter processing on the collected image. The specific method is as follows:

[0041] Place the sealing ring on a transparent glass platform, place a parallel light source above the sealing ring, and the camera is facing the sealing ring to collect the arc segment, turn on the parallel light source, the camera collects the surface image of the sealing ring, and performs adaptive median value on the collected image filter processing. In order to clearly observe the effect of filter processing, select a local area on the surface of the sealing ring such as figure 2 (a), the processing result of adaptive median filtering is as follows figure 2 (b) shown.

[0042] Step 2, use the Sobel template W for the image pro...

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Abstract

The invention discloses a sealing ring surface defect detection method based on machine vision. The sealing ring surface defect detection method comprises the following steps: firstly, acquiring images of the surface of a sealing ring, and performing self-adaptive median filtering treatment on the acquired images; subsequently, calculating a gray level gradient and a vertical gradient of the images, and extracting gray bevel structures in the images according to the gray level gradient and the vertical gradient; partitioning different bevel characteristic point neighborhoods, and calculating a gray level mean of the partitioned neighborhoods; finally, by taking functions for describing the gray level difference degree of the partitioned neighborhoods as defect judgment principles, screening out defect outline points, and detecting the defects of the surface of the sealing ring. According to the forming reason of the defects of the surface of the sealing ring, inherent differences of defect outlines and appearance outline of the sealing ring can be analyzed and verified on the images, various types of defects, including recesses, rill marks, impurities, trimming and over-cutting defects, on the surface of the sealing ring can be detected, and the method has the advantages of high defect detection accuracy, good algorithm robustness and the like.

Description

technical field [0001] The invention belongs to the field of machine vision and digital image processing, and in particular relates to a method for detecting surface defects of a sealing ring based on machine vision. Background technique [0002] The spacecraft sealing system has strict quality requirements for the sealing ring used, and the integrity of its surface directly determines the performance and life of the sealing system, which in turn affects the reliability of the spacecraft in orbit. As the most widely used sealing element, the surface quality inspection of O-shaped rubber sealing ring (O-ring for short) mainly includes flow marks, lack of glue, concave and convex defects, excessive trimming, combined flash, etc. O-ring surface quality control, at present, mostly adopts visual inspection method, and checks with tool microscope or projector after problems are found. This detection method has low efficiency, low precision, and low reliability. [0003] At presen...

Claims

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Application Information

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IPC IPC(8): G01N21/88
CPCG01N21/8851G01N2021/8887
Inventor 何博侠童楷杰
Owner NANJING UNIV OF SCI & TECH
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