Sealing ring classification model training method, sealing ring classification method and device

Abstract

The invention provides a sealing ring classification model training method, a sealing ring classification method and device. The sealing ring classification model training method provided by the invention obtains a sealing ring classification model through training, the sealing ring classification model comprises a plurality of convolutional neural networks and classifiers, and the classifiers areconnected with the convolutional neural networks through the convolutional neural networks. Convolution processing is performed on each part of sample feature data through a plurality of convolutional neural networks, so that a first sample intermediate feature matrix corresponding to each part of sample feature data can be obtained more accurately; therefore, the second sample intermediate feature matrix obtained after fusion processing of the first sample intermediate feature matrix is accurate, and the trained sealing ring classification model is accurate based on the second sample intermediate feature matrix corresponding to each part of sample feature data. According to the sealing ring classification model obtained after training, when the sealing rings are classified, the classification accuracy is high, the types of the sealing rings can be accurately detected, and then the sealing ring detection accuracy is improved.

Description

technical field [0001] The present application relates to the technical field of computer vision, in particular to a training of a sealing ring classification model, a method and a device for classifying sealing rings. Background technique [0002] After the sealing ring is produced or before use, it is necessary to inspect the sealing ring to determine whether the sealing ring has burrs. If there are burrs, the sealing ring is not a good product; if there is no burr, it is a good product. Wherein, the sealing ring includes an O-ring. [0003] In the prior art, since the distance from the edge of the good O-ring to the center of the O-ring is a fixed value, it is possible to determine whether the O-ring has burrs by detecting the distance between the point on the edge of the O-ring and the center of the circle. If the distance from the point to the center of the O-ring exceeds the set threshold range on the edge of the O-ring, it is considered that the O-ring has a burr, th...

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Problems solved by technology

However, when this method is applied to non-rigid O-rings, such as rubber O-rings, the distance from the edge of the O-ring to the center of the O-ring will change due to the deformation of the rubber O-ring. Judging by the above method When the rubber O-ring is a good product, it is easy to judge the good product O-ring that is deformed but has no burrs as a non-good product, which makes the detection result have large errors and the detection accuracy is low.

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