Recognition and positioning method and system based on backlight module and multi-template matching

Abstract

The invention provides an recognition and positioning method and system based on a backlight module and multi-template matching. The method comprises the following steps: carrying out image collection on a target area based on an image collection module to obtain a to-be-recognized sole image; performing image smoothing processing on the to-be-recognized sole image through an image processing module to obtain template contour information of the to-be-recognized sole image; and matching in a preset sole template library based on the outline information of the to-be-recognized sole image template to obtain sole information corresponding to the outline information of the to-be-recognized sole image template. In the automatic production of the shoemaking industry, after the products are fed into the backlight module, the products of different models can be quickly and accurately recognized and positioned by utilizing a machine vision algorithm, cooperating with the backlight module and adopting a multi-template matching algorithm, the operation is simple, and the technical cost is low.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to a recognition and positioning method and system based on backlight module and multi-template matching. Background technique [0002] In the automatic production of the shoemaking industry, the soles are divided into different models, different sizes and left and right feet. Although it is easier to establish the left and right foot templates for each model and size, each template can be collected in the template library, but when the template is called for matching If you select the one with the highest matching degree as the result, you can only get the first few files in the template library, but you cannot add labels to the templates. Using the deep learning algorithm in the field of artificial intelligence, you can add labels to the training templates, but the use of deep learning requires collocation. In the hardware environment, it is difficult to collect a large number of tra...

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Problems solved by technology

[0002] In the automatic production of the shoemaking industry, shoe soles are divided into different models, different sizes, and left and right feet. Although it is relatively easy to create left and right foot templates for each model and size, each template can be collected in the template library, but when the template is called for matching Select the one with the highest matching degree as the result, only the first few files in the template library can be obtained, but the template cannot be tagged, and the deep learning algorithm in the field of artificial intelligence can be used to add tags to the trained template, but using deep learning requires matching In the hardware environment, it is difficult to collect a large number of training samples, and the operation is complicated and costly

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