Bobbin yarn appearance defect classification method based on a deep convolutional neural network
A neural network and deep convolution technology, applied in the field of classification of bobbin appearance defects, can solve the problems of inability to accurately detect the appearance of glass fiber bobbins and low reliability, and achieve the goal of improving reliability and detection speed, and reducing labor costs Effect
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[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0034] The invention provides a method for classifying bobbin appearance defects based on a deep convolutional neural network, which specifically includes the following steps:
[0035] S1. Image cutting: cutting the picture of the bobbin defect, cutting out the part of the suspected defect in the picture and using it as data to be classified;
[0036] S2, manual sorting: the data to be sorted described in step S1 is manually roughly classified according to hai...
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