Rock fragment size classification method based on multiple features and segmentation recorrection

Abstract

The invention relates to a rock fragment size classification method based on multiple features and segmentation recorrection. The method comprises the following steps that (1) an original rock fragment size image is cut and blocked to generate subblock images of 64*43; (2) color feature extraction is carried out on the subblock images; (3) texture feature extraction is carried out on the subblock images; (4) feature selection is carried out on color features and texture features; (5) a classifier is designed through a support vector machine (SVM), and rough classification on the subblock images is completed; (6) watershed segmentation is carried out on the original rock fragment size image, and each obtained rock area is marked as blob; (7) on the basis of segmentation, a result obtained through rough classification is corrected and adjusted to improve classification performance and obtain a final classification result. According to the classification method, different classification algorithms are synthesized, efficiency and accuracy are high, cost is low, and the real-time performance requirement of a mining field is met.

Description

Technical field [0001] The invention relates to a method for ore rock fragmentation classification based on multiple features and segmentation and re-correction. Background technique [0002] In the process of mining processing and production, the block shape and size of the ore mined by blasting have a great impact on subsequent production, for example, the impact on production costs is as high as 30%. Usually rock classification is determined by sieving in the laboratory and manual measurement. This traditional manual measurement method has problems such as low efficiency, low accuracy, or high cost, and cannot meet the real-time requirements of the mining site, so it is difficult to adapt to the needs of modern mechanical automation and computer intelligent production. To overcome these difficulties, it is necessary to improve the monitoring system in production and apply machine vision and computer image processing technology to the system. The computer monitoring system ca...

AI Technical Summary

Problems solved by technology

The above research has solved some problems in the analysis and classification of rock blocks based on image processing technology, and has provided guidance for industrial production. However, the application range of its algorithm is not wide enough, so it needs to be studied in depth and explore more new methods to continuously improve and Improve the accuracy of recognition and classification

At the same time, there are many rock blocks in the image of complex ore blocks, the size distribution is uneven, and they are piled up on each other. Moreover, the surface of the rock blocks is rough, which is easy to produce shadows, resulting in inaccurate image classification results. In order to accurately describe the characteristics of complex rock blocks, it is necessary to design a comprehensive Different classification algorithms using various features

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