Defect detection method and system for battery surface identification based on machine vision

Abstract

The invention discloses a defect detection method and system for battery surface identification based on machine vision, belonging to the technical field of machine vision detection, by acquiring andpre-processing images of the battery to be tested in a suitable light source environment, the best effect image and posture image of the battery are obtained. The character region and two-dimensionalcode region in the posture image were selected by fast frames, and the OCR character detection and matching, two-dimensional code recognition are carried out quickly to judge whether the battery surface printing is qualified or not. According to the invention the pre-processed images are corrected to obtain the optimal posture image, which greatly saves time for the subsequent operation of the automatic positioning characteristic region, the character region and the two-dimensional code region are framed out more quickly and accurately, the detection result can be quickly and accurately judged, and whether the battery surface printing is qualified or not is determined.

Description

technical field [0001] The invention relates to a battery defect detection method and system, in particular to a machine vision-based battery surface mark defect detection method and system, belonging to the technical field of machine vision detection. Background technique [0002] Today's industry involves product size measurement, product positioning recognition, and product appearance defect detection. Most of them use machine vision technology to convert the captured object into an image signal through an image capture device, and then send it to the image processing system. According to the pixel characteristics The image signal is converted into a digital signal, and the image processing system uses these digital signals to extract target features, and judges the product result according to the feature data for quality inspection. However, the existing mobile phone battery surface mark defect detection system has high power consumption, low efficiency, The disadvantage...

AI Technical Summary

Problems solved by technology

[0002] Today's industry involves product size measurement, product positioning recognition, and product appearance defect detection. Most of them use machine vision technology to convert the captured object into an image signal through an image capture device, and then send it to the image processing system. According to the pixel characteristics The image signal is converted into a digital signal, and the image processing system uses these digital signals to extract target features, and judges the product result according to the feature data for quality inspection. However, the existing mobile phone battery surface mark defect detection system has high power consumption, low efficiency, Disadvantages such as low error detection accuracy

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