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Comparison of 3D Object Detection Methods for People Detection in Underground Mine.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Comparison of 3D Object Detection Methods for People Detection in Underground Mine./
Author:	Yuwono, Yonas Dwiananta.
Description:	1 online resource (79 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-04.
Contained By:	Masters Abstracts International84-04.
Subject:	Mining. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29068395click for full text (PQDT)
ISBN:	9798352691106

Comparison of 3D Object Detection Methods for People Detection in Underground Mine.
Yuwono, Yonas Dwiananta.

Comparison of 3D Object Detection Methods for People Detection in Underground Mine. - 1 online resource (79 pages)

Source: Masters Abstracts International, Volume: 84-04.

Thesis (M.S.)--Colorado School of Mines, 2022.

Includes bibliographical references

Autonomous vehicles have received immense attention in the mining industry nowadays. However, there is limited research on 3D object detection in the underground mine. This thesis wants to compare the ability of 3D object detection models in the underground mine environment. Three state-of-the-art 3D object detections are analyzed to detect people in the underground mine. The author collects 1000 point cloud files from Edgar mine to train and test the algorithm performance. Data labeling and preprocessing methods are discussed to convert raw point clouds to the algorithm's format. Then, several training parameters such as the number of datasets and epochs are analyzed to obtain the maximum performance of object detection methods. PV-RCNN has the highest average precision for the study case in the underground mine. All datasets, source code, and train test split are accessible at https://github.com/karana0103/EdgarObjDetection for future use cases.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352691106Subjects--Topical Terms:

3544442
Mining.
Subjects--Index Terms:

3D object detectionIndex Terms--Genre/Form:

542853
Electronic books.

Comparison of 3D Object Detection Methods for People Detection in Underground Mine.
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245 1 0 $a Comparison of 3D Object Detection Methods for People Detection in Underground Mine.
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300 $a 1 online resource (79 pages)
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500 $a Source: Masters Abstracts International, Volume: 84-04.
500 $a Advisor: Duzgun, Sebnem.
502 $a Thesis (M.S.)--Colorado School of Mines, 2022.
504 $a Includes bibliographical references
520 $a Autonomous vehicles have received immense attention in the mining industry nowadays. However, there is limited research on 3D object detection in the underground mine. This thesis wants to compare the ability of 3D object detection models in the underground mine environment. Three state-of-the-art 3D object detections are analyzed to detect people in the underground mine. The author collects 1000 point cloud files from Edgar mine to train and test the algorithm performance. Data labeling and preprocessing methods are discussed to convert raw point clouds to the algorithm's format. Then, several training parameters such as the number of datasets and epochs are analyzed to obtain the maximum performance of object detection methods. PV-RCNN has the highest average precision for the study case in the underground mine. All datasets, source code, and train test split are accessible at https://github.com/karana0103/EdgarObjDetection for future use cases.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Mining. $3 3544442
650 4 $a Computer science. $3 523869
653 $a 3D object detection
653 $a People detection
653 $a Underground mines
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0551
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Colorado School of Mines. $b Mining Engineering. $3 3341046
773 0 $t Masters Abstracts International $g 84-04.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29068395 $z click for full text (PQDT)