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Intelligent Extraction of Minimum Burden in Medium-Length Hole Blasting Using Combined Region Growing and DBSCAN
Yu Bai1, Yachun Mao1, Shuai Zhen1
1College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
This study introduces an automatic method for extracting minimum burden in open-pit mines using UAV data and combined region growing and DBSCAN algorithms. The approach accurately identifies slope surfaces and calculates minimum burden, improving blasting control.
Area of Science:
- Geotechnical Engineering
- Mining Engineering
- Computer Science
Background:
- Directly measuring minimum burden in open-pit mine blasting is challenging.
- Existing single-algorithm methods lack accuracy for minimum burden extraction.
Purpose of the Study:
- To develop an automatic extraction method for minimum burden using combined region growing and DBSCAN.
- To improve the accuracy of minimum burden measurement in open-pit mines.
Main Methods:
- Utilized UAV-acquired 3D point cloud data.
- Applied region growing and DBSCAN for slope surface reconstruction.
- Calculated minimum burden using 3D borehole modeling and shortest Euclidean distance.
Main Results:
- Effectively identified slope free surfaces and accurately extracted minimum burden.
- Achieved an average absolute error of 0.077 m and relative error of 2.68% in the Huatailong mine.
- Demonstrated reliable performance across multiple open-pit mine sites.
Conclusions:
- The proposed method offers a robust solution for minimum burden extraction.
- Provides a reliable basis for blasting fragmentation control and blast-hole pattern design.
