Method and system for detecting bad appearance of product and storage medium

Abstract

The invention provides a method for detecting the bad appearance of a product, and the method comprises the following steps: obtaining a product appearance image, carrying out the graying of the product appearance image, and obtaining a first gray level image; binarizing the first grayscale image to obtain a second grayscale image; performing contour extraction on the second grayscale image, and screening to obtain a contour combination; obtaining a minimum bounding rectangle of the contour combination, completing appearance feature classification of the product appearance image through a neural network according to the minimum bounding rectangle, and identifying a product with poor appearance according to a classification result. According to the invention, contour extraction is carried out by sequentially carrying out graying and binaryzation, so that the reduction of the recognition accuracy caused by the influence of light is avoided; and by combining the minimum bounding rectangleof the contour combination with the neural network, the occupation of operation resources is reduced, the image processing speed is at a millisecond level, the operation real-time performance is relatively high, the detection effect is relatively good, automatic identification and testing can be realized, and the invention can be widely applied to the technical field of product quality detection.

Description

technical field [0001] The invention relates to the technical field of product quality inspection, in particular to a method, system and storage medium for detecting defective product appearance. Background technique [0002] On the production line, various defective products will appear with different probabilities in mass production. Some of them are products with defective appearance. If they pass manual screening, it will consume a lot of manpower. [0003] In the existing technology, most of them adopt the method of installing an auxiliary detection system based on a camera and a recognition algorithm to realize automatic identification of defective products. However, the existing recognition system is easily affected by light, and poor lighting effect will lead to poor recognition effect. Complicated programs can improve the recognition effect of a single image, but have the disadvantage of taking too long to recognize. For different machines, the traditional image ...

AI Technical Summary

Problems solved by technology

[0005] 1. The test results are easily affected by light;

[0006] 2. The program with better recognition effect runs slowly, and the remote debugging of the algorithm is difficult;

[0007] 3. Local calculation effect, the accuracy of recognition is low

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