Mark detection model training and mark detection method based on multi-stage transfer learning

Abstract

The invention belongs to the field of computer vision, particularly relates to a mark detection model training and mark detection method, system and device based on multi-stage transfer learning, andaims to solve the problem of low detection accuracy of an existing mark detection model due to few mark samples. The system model training method comprises the steps of pre-training a mark detection model based on a sample selected in an ImageNet data set to obtain a first model; performing fine adjustment training on the first model based on the synthetic mark sample to obtain a second model; training the second model based on the real mark sample to obtain a third model; and taking the third model as a trained mark detection model. The detection method comprises the following steps: acquiring a to-be-detected mark image; and carrying out target mark detection on the mark image through the mark detection model obtained by the model training method. According to the invention, the number of mark samples is increased, and the detection accuracy of the mark detection model is improved.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a multi-stage transfer learning-based mark detection model training and mark detection method, system and device. Background technique [0002] Although many excellent target detection research results have emerged now, due to the high background richness of different attachments in different scenes and different attachments in the real world, the signs in them are of various colors, shapes, and vary in thousands of ways, making sign detection difficult. The problem itself is of high difficulty. In addition, with the dependence of deep learning models on large data sets in recent years, the problem of sign detection in the case of a small number of samples is even more difficult. [0003] Training a huge deep learning model requires huge data sets as support, so the primary problem of using deep learning algorithms for sign detection is the problem of large-scale data ...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the problem of low detection accuracy of the existing mark detection model due to the small number of mark samples, the first aspect of the present invention proposes a mark detection model based on multi-stage transfer learning A training method comprising:

Images