scholarly journals Topographic Survey at Small-scale Open-pit Mines using a Popular Rotary-wing Unmanned Aerial Vehicle (Drone)

2015 ◽  
Vol 25 (5) ◽  
pp. 462-469 ◽  
Author(s):  
Sungjae Lee ◽  
Yosoon Choi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Open Pit ◽  
Small Scale ◽  
Open Pit Mines ◽  
Topographic Survey ◽  
Aerial Vehicle ◽  
Rotary Wing

Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

2019 ◽  
Vol 29 (2) ◽  
pp. 771-790 ◽  
Author(s):  
Xuan-Nam Bui ◽  
Yosoon Choi ◽  
Victor Atrushkevich ◽  
Hoang Nguyen ◽  
Quang-Hieu Tran ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Ground Vibration ◽  
Open Pit ◽  
Open Pit Mines ◽  
Vibration Intensity ◽  
Intelligence System ◽  
Aerial Vehicle

scholarly journals Continuous Monitoring and Improvement of the Blasting Process in Open Pit Mines Using Unmanned Aerial Vehicle Techniques

2020 ◽  
Vol 12 (17) ◽  
pp. 2801
Author(s):  
Thomas Bamford ◽  
Filip Medinac ◽  
Kamran Esmaeili
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Speed ◽  
Field Experiments ◽  
Block Size ◽  
Structural Data ◽  
Open Pit ◽  
Open Pit Mines ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Technical Manpower

The current techniques used for monitoring the blasting process in open pit mines are manual, intermittent and inefficient and can expose technical manpower to hazardous conditions. This study presents the application of unmanned aerial vehicle (UAV) systems for monitoring and improving the blasting process in open pit mines. Field experiments were conducted in different open pit mines to assess rock fragmentation, blast-induced damage on final pit walls, blast dynamics and the accuracy of blastholes including production and pre-split holes. The UAV-based monitoring was done in three different stages, including pre-blasting, blasting and post-blasting. In the pre-blasting stage, pit walls were mapped to collect structural data to predict in situ block size distribution and to develop as-built pit wall digital elevation models (DEM) to assess blast-induced damage. This was followed by mapping the production blasthole patterns implemented in the mine to investigate drillhole alignment. To monitor the blasting process, a high-speed camera was mounted on the UAV to investigate blast initiation, sequencing, misfired holes and stemming ejection. In the post-blast stage, the blasted rock pile (muck pile) was monitored to estimate fragmentation and assess muck pile configuration, heave and throw. The collected aerial data provide detailed information and high spatial and temporal resolution on the quality of the blasting process and significant opportunities for process improvement. The current challenges with regards to the application of UAVs for blasting process monitoring are discussed, and recommendations for obtaining the most value out of an UAV application are provided.

scholarly journals Experimental Investigation on the Performance of DJI Phantom 4 RTK in the PPK Mode for 3D Mapping Open-Pit Mines

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Le VAN CANH ◽  
Cao XUAN CUONG ◽  
Nguyen QUOC LONG ◽  
Le THI THU HA ◽  
Tran TRUNG ANH ◽  
...  
Keyword(s):  
Coal Mines ◽  
Daily Basis ◽  
Open Pit ◽  
Surface Model ◽  
Test Field ◽  
Open Pit Mines ◽  
Ground Control Points ◽  
Aerial Vehicle ◽  
Topographic Surveys

Open-pit coal mines’ terrain is often complex and quickly and frequently changes. Therefore, topographic surveys of open-pit mines are undertaken on a daily basis. While these tasks are very time-consuming and costly with traditional methods such as total station and GNSS, the unmanned aerial vehicle (UAV) based method can be more efficient. This method is a combination of the “Structure from motion” (SfM) photogrammetry technique and UAV photogrammetry which has been widely used in topographic surveying. With an increasing popularity of RTK-enabled drones, it is becoming even more powerful method. While the important role of ground control points (GCP) in the accuracy of digital surface model (DSM) generated from images acquired by “traditional” UAVs (not RTK-enabled drones) has been proved in many previous studies, it is not clear in the case of RTK-enabled drones, especially for complex terrain in open-pit coal mines. In this study, we experimentally investigated the influence of GCP regarding its numbers and distribution on the accuracy of DSM generation from images acquired by RTK-enabled drones in open-pit coal mines. In addition, the Post Processing Kinematic (PPK) mode was executed over a test field with the same flight altitude. DSM generation was performed with several block control configurations: PPK only, PPK with one GCP, and PPK with two GCPs. Several positions of GCPs were also examined to test the optimal locations for placing GCPs to achieve accurate DSMs. The results show that the horizontal and vertical accuracy given by PPK only were 9.3 and 84.4 cm, respectively. However, when adding at least one GCP, the accuracy was significantly improved in both horizontal and vertical components, with RMSE for XY and Z ranging between 3.8 and 9.8 cm (with one GCP) and between 3.0 and 5.7 cm (with two GCPs), respectively. Also, the GCPs placed in the deep areas of the open-pit mine could ensure the cm-level accuracy.

scholarly journals UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2238 ◽  
Author(s):  
Mingjie Liu ◽  
Xianhao Wang ◽  
Anjian Zhou ◽  
Xiuyuan Fu ◽  
Yiwei Ma ◽  
...  
Keyword(s):  
Object Detection ◽  
Unmanned Aerial Vehicle ◽  
Spatial Information ◽  
Detection Method ◽  
Small Scale ◽  
Small Object ◽  
Aerial Vehicle ◽  
Public Dataset ◽  
Small Object Detection ◽  
Level Performance

Object detection, as a fundamental task in computer vision, has been developed enormously, but is still challenging work, especially for Unmanned Aerial Vehicle (UAV) perspective due to small scale of the target. In this study, the authors develop a special detection method for small objects in UAV perspective. Based on YOLOv3, the Resblock in darknet is first optimized by concatenating two ResNet units that have the same width and height. Then, the entire darknet structure is improved by increasing convolution operation at an early layer to enrich spatial information. Both these two optimizations can enlarge the receptive filed. Furthermore, UAV-viewed dataset is collected to UAV perspective or small object detection. An optimized training method is also proposed based on collected UAV-viewed dataset. The experimental results on public dataset and our collected UAV-viewed dataset show distinct performance improvement on small object detection with keeping the same level performance on normal dataset, which means our proposed method adapts to different kinds of conditions.

Performance Analysis in Object Diameter Estimation based on Single and Multiple Non-Destructive and Discrete Infrared Sensors

2014 ◽  
Vol 67 (1) ◽  
Author(s):  
Norashikin M. Thamrin ◽  
Norhashim Mohd. Arshad ◽  
Ramli Adnan ◽  
Rosidah Sam ◽  
Noorfazdli Abd. Razak ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Pass Rate ◽  
Small Scale ◽  
Infrared Sensors ◽  
Scanning Method ◽  
Average Percentage ◽  
Multiple Sensors ◽  
Localization And Mapping ◽  
Aerial Vehicle

In Simultaneous Localization and Mapping (SLAM) technique, recognizing and marking the landmarks in the environment is very important. Therefore, in a commercial farm, rows of trees, borderline of rows as well as the trees and other features are mostly used by the researchers in realizing the automation process in this field. In this paper, the detection of the tree based on its diameter is focused. There are few techniques available in determining the size of the tree trunk inclusive of the laser scanning method as well as image-based measurements. However, those techniques require heavy computations and equipments which become constraints in a lightweight unmanned aerial vehicle implementation. Therefore, in this paper, the detection of an object by using a single and multiple infrared sensors on a non-stationary automated vehicle platform is discussed. The experiments were executed on different size of objects in order to investigate the effectiveness of this proposed method. This work is initially tested on the ground, based in the lab environment by using an omni directional vehicle which later will be adapted on a small-scale unmanned aerial vehicle implementation for tree diameter estimation in the agriculture farm.  In the current study, comparing multiple sensors with single sensor orientation showed that the average percentage of the pass rate in the pole recognition for the former is relatively more accurate than the latter with 93.2 percent and 74.2 percent, respectively. 

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