Image quality detection method, equipment and equipment and storage medium

Abstract

The invention relates to the field of artificial intelligence, provides an image quality detection method, equipment and equipment and a storage medium, can introduce tensor norm features into a neural network, enriches the form of the neural network, enriches the feature space of the neural network, improves the generalization ability of the network, increases the expression ability of a model, and improves the accuracy of image quality detection. Therefore, the effect of the model is improved, the output data of the image quality detection model is collected as an image quality detection result, image quality detection is automatically performed based on the improved model, the detection effect is effectively ensured, and a powerful data basis is provided for solving the problem of machine vision. The method can also be applied to scenes such as intelligent transportation and intelligent safety and protection, so that the construction of a intelligent city is promoted. In addition, the invention also relates to a blockchain technology, and an image quality detection result can be stored in a blockchain node.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to an image quality detection method, device, equipment and storage medium. Background technique [0002] Existing neural network designs mostly innovate on the network structure or on the activation function. The former is performed on the macrostructure, while the latter is performed on the constituent units. However, part of the constituent units (i.e., neurons) is a linear unit, and the calculation method of the inner product is mainly used, but the inner product needs the guidance of the template (i.e., the parameters of the neuron) when expressing the similarity, and the template has component alignment The problem. [0003] Moreover, due to the use of the inner product linear unit, the current neural network always faces many problems when it is applied to CV (Computer Vision, computer vision) problems, such as scale problems, rotation invariance pro...

AI Technical Summary

Problems solved by technology

However, part of the constituent units (i.e., neurons) is a linear unit, and the calculation method of the inner product is mainly used, but the inner product needs the guidance of the template (i.e., the parameters of the neuron) when expressing the similarity, and the template has component alignment The problem

[0003] Moreover, due to the use of the inner product linear unit, the current neural network always faces many problems when it is applied to CV (Computer Vision, computer vision) problems, such as scale problems, rotation invariance problems, etc.

There is a certain mismatch between the inner product linear unit and the diverse signals, which is one of the reasons why the current network parameters are usually larger than the training data, and it also affects the expressive ability and generalization ability of the network to a certain extent.

[0004] As an important field of CV problems, image quality detection is mainly applied to statistical features, but neural networks have universal approximation characteristics. If the best features are several statistics, training by deep learning requires many layers of networks to be realized, and training is difficult. higher

Images