China National Institute of Standardization

Jingna Yang

Yuwei Lu

Jingxing Liao

Yunjie Zhi

Disclosed is a method for determining a number of product quality incidents based on a convolutional neural network, the method includes the following steps: building an appearance quality dataset, building a quality evaluation model, and analyzing a number of product quality incidents; a preset model and a preset optimized model are trained and validated according to the built appearance quality dataset to obtain initial quality evaluation models, which are filtered to obtain a quality evaluation model according to model performance coefficient and comprehensive model coefficient, product appearance defect data are obtained according to appearance defect data detected and obtained by the quality evaluation model, appearance defect ratio and significant appearance defect ratio of the products under quality inspection are obtained according to the product appearance defect data, and a number of product quality incidents of the products under quality inspection is finally obtained according to an incident occurrence coefficient.

Method for determining number of product quality incidents based on convolutional neural network

The present invention is related to the field of quality control technology, specifically disclosing a classification method for product quality accidents and its system based on convolutional neural networks. The method consists of: collecting the sample data of product quality accidents, and grading the severity of accidents for each product with the quality accident; then extracting the image feature elements of various products with quality accidents and a valid feature element screening is performed; after that, a product quality accident classification model can be generating through training various valid feature elements and then the product quality accident is classified. The present invention can reduce the subjective errors due to human judgment, and thus the objectivity and consistency of the quality accident assessment results can be improved.

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Beijing, China

Jingna Yang