Detection method for copper grade of copper block, and detection system thereof

Abstract

The invention discloses a detection method for a copper grade of a copper block. The method comprises the steps of (1) acquiring images and extracting ROI; (2) extracting texture features from the ROI; (3) judging quality of the ROI; (4) calculating a color vector angle; (5) determining types of a to-be-detected copper block; and (6) calculating the copper grade. The detection method can automatically finish copper block section positioning and ROI acquisition under a complex background, select out the ROI with qualified quality base on the texture features and finally estimate the copper grade rapidly by mixed Gaussian regression with relatively high precision. The invention also discloses a detection system for the copper grade. The detection system is based on an embedded platform of ARM + DSP dual-core chips and acquires the image by using an industrial camera. The detection system is easy to carry, greatly reduces cost, is very suitable for productization, and has the advantages of full automation, no contact, no damage, continuation, real time and high accuracy.

Description

technical field [0001] The invention belongs to the technical field of metallurgical process testing, and in particular relates to a copper grade detection method of a copper block and a detection system thereof. Background technique [0002] The copper processing process needs to be able to quickly and accurately measure the quality index of the copper block about the copper grade of the sample. The online detection of copper grade is very important for judging whether the redox process is completed in a timely and efficient manner. [0003] In copper processing, copper grade is a key quality parameter for automatic control. However, due to factors such as high temperature and corrosion, online detection of this parameter has become a technical problem in the application of complete control systems. Under the existing technology, copper grade detection generally adopts the detection method of offline sampling inspection and sending to the laboratory for testing, and most o...

AI Technical Summary

Problems solved by technology

[0008] (1) It is based on an industrial control computer platform and uses an industrial camera with a Gigabit network card interface. The cost is relatively high, and the portability is insufficient, so it is not suitable for productization and promotion;

[0009] (2) The extraction of ROI (Region of Interest) all relies on manual selection, which is troublesome to operate;

[0010] (3) There is no copper section quality detection mechanism for the copper block sample to be tested. If there are many scratches or stains in the ROI, the reliability of the test results will be low;

[0011] (4) The regression adopts the least squares support vector machine model, which has no physical meaning. For example, the grade span of brass and red copper is very large, and the regression together will greatly affect the accuracy of the model.

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