Method for establishing scrap steel grading neural network model

Abstract

The invention discloses a method for establishing a scrap steel grading neural network model. The model is used for grade classification detection of scrap steel collection and storage. Including acquiring a plurality of images, determining different scrap steel grades of the plurality of images by visual inspection, preprocessing the images to remove invalid watermarks, improving image contrast,extracting image data characteristics of image data, and performing convolutional neural network learning on the extracted image data characteristics of different grades to form a graded neural network model with grade classification output; wherein the extraction of the image data features is realized by a set obtained by carrying out convolutional neural network convolution calculation on pixelmatrix data of an image picture; the method comprises the steps of extracting object colors, edge features and texture features in an image and extracting associated features between object edges andtextures in the image, wherein the object colors, the edge features and the texture features are formed by calculating a plurality of circuit convolution layers or convolution layers and pooling layers output by a set.

Description

technical field [0001] The invention relates to a method for establishing a neural network model for classifying scrap steel. Background technique [0002] Steel mills collect and store a large amount of steel scrap every year. Except for waste machinery and equipment, it is mainly a large amount of scrap steel fed in by cars. Shredded steel includes pipes, blocks and plates of various shapes, with different sizes and shapes. , which is also mixed with other impurities. The imported shredded steel needs to be graded according to the thickness and paid according to the grade. Therefore, the grade approval is very important. At present, the quality inspection process is to manually receive the material, and manually fill in the receipt notice In the process of quality inspection, quality inspectors are required to climb onto the scrap truck, manually measure the size of scrap steel, manually inspect the quality of scrap steel, and judge the grade of scrap steel, and then manua...

AI Technical Summary

Problems solved by technology

This approach has the following disadvantages: 1) Scrap steel has different shapes and piles up messily, and it is impossible to check the overall scrap steel situation in detail

2) Since the inspectors need to get on and off the car frequently, there will be potential safety hazards

3) The production pace of the steel mill is fast, and the quality inspectors frequently get on and off the car for inspection, which affects the unloading progress of the crane and the production process

4) It is necessary to set up a special person to do this work, and there is a labor cost problem

5) Manual transcription and inspection are all based on visual inspection. The standards vary from person to person and are not uniform. There are misjudgments and misjudgments, and the accuracy rate is not high

6) There is a risk of private communication between suppliers and quality inspectors

[0003] As an image recognition technology, convolutional neural network has been widely used in face recognition. By establishing a recognition model and inputting face images into the model for recognition, can this method also be used to classify broken steel grades? However, in The process of establishing a face recognition model is usually learned by extracting the edge features of the face, and the crushed steel materials shipped as cars are overlapped and extruded together, and some small crushed steel materials are mixed together It is impossible to distinguish its shape when it is laid in the carriage, so it is impossible to build a model by using the conventional method of extracting edge features

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