Real-time detection method of polished metal arc-shaped surface defects based on machine vision

Abstract

The invention relates to a real-time detecting method of metal arc plane flaws based on machine vision. The method comprises the following steps: step 1. obtaining N first sample images and M second sample images under the off-line condition, and carrying out data combination; step 2. establishing an image histogram after the combination so as to obtain a linear relationship equation between an image background and a corresponding gray value; step 3. calculating a refection component of a first sample image and a second sample image selected; step 4. establishing a corresponding relationship equation between a qualified working refection component standard difference and a corresponding gray value; step 5. on-line obtaining a detection image of a polished metal arc plane workpiece at real time, and calculating a first standard difference and a second standard difference; step 6. carrying out threshold partitioning so as to obtain a corresponding binary image; and step 7. comparing the connected region area of the binary image with a preset judgment threshold so as to judge the flaws of the polished metal arc plane. According to the method, the operation is convenient, the detection precision is high, the detection adaptability is good, and the stability and the reliability are realized.

Description

technical field [0001] The invention relates to a detection method, in particular to a machine vision-based real-time detection method for flaws on polished metal arcuate surfaces, belonging to the technical field of polished metal arcuate surface detection. Background technique [0002] In industrial production, with the continuous improvement of industrial technology, the quality requirements of products also increase. The traditional monitoring of workpiece quality mainly depends on manual visual inspection. Due to the influence of the subjective factors of the inspectors, it is easy to have false detections and missed detections, and manual visual inspection has low efficiency, low accuracy and insufficient standardization. The detection results are closely related to the personal ability and mental state of the inspectors. , the stability and reliability are relatively poor. In addition, the detection data classification cannot be sent to the computer in real time for a...

AI Technical Summary

Problems solved by technology

There are still several problems in the detection of such workpieces: (1) Due to the high reflectivity of the surface of the polished arc-shaped workpiece, the reflection angle of the reflected light received by the camera is different, so it is difficult to obtain an image with uniform illumination and a wide format; ( 2) The gray distribution of the collected image is uneven, and the effective area is too narrow

But when the workpiece has a large defect, the extraction of the background image will have errors

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