High-dimensional image target defect detection model based on axial self-attention

Abstract

The invention relates to a target defect detection model based on a small sample high-dimensional image. By introducing an axial self-attention mechanism, a target defect detection model based on a small sample high-dimensional image is designed for solving the problems that a traditional deep convolutional neural network is large in occupied memory, slow in processing time and incapable of effectively solving the target detection problem of a high-dimensional high-definition image in a professional field during image feature extraction. According to the model, effective global representation extraction can be carried out on a high-dimensional picture, the calculation complexity is remarkably reduced, the performance of small sample high-dimensional image target detection is improved, and the requirements of practical application problems are met. According to the target defect detection model based on the small sample high-dimensional image, the problems of slow time and poor effect of traditional deep convolution extraction of the depth global representation of the high-dimensional image are solved, and the method can be effectively used for the practical application problems of defect target detection and the like of the high-dimensional image in a small sample situation.

Description

technical field [0001] The invention belongs to the field of computer vision and machine learning, and relates to a target detection and image processing method, in particular to a target defect detection model with high mobility based on an axial self-attention mechanism. Background technique [0002] Object detection is a classic problem in the field of computer vision and machine learning. Especially in professional fields such as medicine and industrial troubleshooting, it is a common method to assist experts in decision-making through high-dimensional high-definition images. The traditional target defect detection is basically checked one by one manually by experts in the corresponding field. Although the reliability is high, it has the disadvantages of slow detection speed and high cost. The current method based on machine learning model is to use a convolutional neural network model on the artificially collected defect target data set, and use a multi-class discrimin...

AI Technical Summary

Problems solved by technology

[0003] Aiming at the problem that the existing target detection model based on convolutional neural network cannot efficiently process high-dimensional and high-resolution pictures, and the problem of sacrificing a large amount of memory or global information of pictures based on self-attention mechanism or compressed self-attention mechanism, the present invention proposes A target defect model centered on the axial self-attention mechanism

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