Ore extraction and analysis from RGB image and 3D Point Cloud
Feng Jin 1,2
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BGRIMM Technology Group
University of Science and Technology Beijing
Submission date: 2021-12-20
Final revision date: 2022-02-18
Acceptance date: 2022-02-28
Publication date: 2022-03-23
Corresponding author
Shuwei Huang   

BGRIMM Technology Group
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2022;38(1):89-105
Based on the theory of computer vision, a new method for extracting ore from underground mines is proposed. This is based on a combination of RGB images collected by a color industrial camera and a point cloud generated by a 3D ToF camera. Firstly, the mean-shift algorithm combined with the embedded confidence edge detection algorithm is used to segment the RGB ore image into different regions. Secondly, the effective ore regions are classified into large pieces of ore and ore piles consisting of a number of small pieces of ore. The method applied in the classification process is to embed the confidence into the edge detection algorithm which calculates edge distribution around ore regions. Finally, the RGB camera and the 3D ToF camera are calibrated and the camera matrix transformation of the two cameras is obtained. Point cloud fragments are then extracted according to the cross-calibration result. The geometric properties of the ore point cloud are then analysed in the subsequent procedure.
This work was jointly supported by the Major Science and Technology Innovation Project of Shandong Province (No. 2019SDZY05) and the Scientific Research Fund of BGRIMM Technology Group (No. 02-2035).
Wydobycie i analiza rudy z obrazu RGB i chmury punktów 3D
obraz rudy, chmura punktów 3D, wbudowane wykrywanie krawędzi ufności, zmiana średniej, kalibracja krzyżowa
W oparciu o teorię widzenia komputerowego zaproponowano nową metodę wydobycia rudy z podziemnych kopalń. Jest to połączenie obrazów RGB zebranych przez kolorową kamerę przemysłową oraz chmury punktów wygenerowanej przez kamerę 3D ToF. Po pierwsze, algorytm zmiany średniej w połączeniu z wbudowanym algorytmem wykrywania krawędzi ufności służy do segmentacji obrazu rudy RGB na różne regiony. Po drugie, efektywne regiony rud są podzielone na złoża rudy o dużych rozmiarach i zwałowiska składające się z małej ilości rudy. Metodą stosowaną w procesie klasyfikacji jest określenie ufności w algorytmie wykrywania krawędzi, który oblicza rozkład krawędzi wokół regionów rudnych. Wreszcie, kamera RGB i kamera 3D ToF są skalibrowane i uzyskuje się transformację matrycy obu kamer. Następnie, fragmenty chmury punktów są wyodrębniane zgodnie z wynikiem kalibracji krzyżowej. W kolejnej procedurze przeanalizowano właściwości geometryczne chmury punktów rudy.
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