Machine vision algorithm for appearance inspection

Abstract

The invention relates to a machine vision algorithm for appearance detection. The machine vision algorithm is used for appearance detection of the outer surface of a product which is smooth, opaque and consistent in main body color. The algorithm provided by the invention has the characteristics of high generalization, high accuracy and sustainable optimization, and can process two main requirements: 1) detecting the color of a product; 2) detecting the appearance of the product. According to the method, multiple algorithms are integrated, the accuracy is high, parameters can be optimized by using historical data, the SIFT image matching algorithm has excellent performance, the generalization is high, and the method is suitable for products with consistent main body colors.

Description

technical field [0001] The invention relates to a machine vision algorithm for appearance detection, which can be used for the appearance detection of the outer surface of products with smooth, opaque and consistent main color. Background technique [0002] Machine vision technology is mainly a recognition technology based on product feature information. It collects images or video streams containing products through cameras or cameras, and automatically detects and tracks them in the images, and then characterizes the detected products. A series of related technologies for comparison. [0003] At present, in China, manual inspection is still used for the appearance inspection of most smooth, opaque, and consistent main color products. [0004] At present, there are related patents related to the hardware design of appearance inspection and other products or general appearance inspection methods, but there is no machine vision inspection algorithm with strong generality. F...

AI Technical Summary

Problems solved by technology

[0007] The above technical solution has the following problems and disadvantages: 1) The template matching method locates the cigarette detection area, which requires a large amount of calculation, and the accuracy of selecting the target area is low under complex background conditions, and it is difficult to keep the relative position of the cigarette in the selected area consistent

2) Select frequency domain features and give up geometric features, it is difficult to judge small defects, and it is impossible to defect the defect location

3) Judging by using the grayscale image, abandoning the three-channel feature of the color, color flaws or flaws whose grayscale value is similar to the main color cannot be accurately judged

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