Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Jingdao | - |
dc.contributor.author | Fang, Yihai | - |
dc.contributor.author | Cho, Yong K. | - |
dc.contributor.author | Kim, Changwan | - |
dc.date.available | 2019-03-08T09:36:40Z | - |
dc.date.issued | 2017-03 | - |
dc.identifier.issn | 0887-3801 | - |
dc.identifier.issn | 1943-5487 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/4750 | - |
dc.description.abstract | Recognizing construction assets (e.g.,materials, equipment, labor) from point cloud data of construction environments provides essential information for engineering and management applications including progress monitoring, safety management, supply-chain management, and quality control. This study introduces a novel principal axes descriptor (PAD) for construction-equipment classification from point cloud data. Scattered as-is point clouds are first processed with downsampling, segmentation, and clustering steps to obtain individual instances of construction equipment. A geometric descriptor consisting of dimensional variation, occupancy distribution, shape profile, and plane counting features is then calculated to encode three-dimensional (3D) characteristics of each equipment category. Using the derived features, machine learning methods such as k-nearest neighbors and support vector machine are employed to determine class membership among major construction-equipment categories such as backhoe loader, bulldozer, dump truck, excavator, and front loader. Construction-equipment classification with the proposed PAD was validated using computer-aided design (CAD)-generated point clouds as training data and laser-scanned point clouds from an equipment yard as testing data. The recognition performance was further evaluated using point clouds from a construction site as well as a pose variation data set. PAD was shown to achieve a higher recall rate and lower computation time compared to competing 3D descriptors. The results indicate that the proposed descriptor is a viable solution for construction-equipment classification from point cloud data. (C) 2016 American Society of Civil Engineers. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ASCE-AMER SOC CIVIL ENGINEERS | - |
dc.title | Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds | - |
dc.type | Article | - |
dc.identifier.doi | 10.1061/(ASCE)CP.1943-5487.0000628 | - |
dc.identifier.bibliographicCitation | JOURNAL OF COMPUTING IN CIVIL ENGINEERING, v.31, no.2 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000395521200012 | - |
dc.identifier.scopusid | 2-s2.0-85013004977 | - |
dc.citation.number | 2 | - |
dc.citation.title | JOURNAL OF COMPUTING IN CIVIL ENGINEERING | - |
dc.citation.volume | 31 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Object recognition | - |
dc.subject.keywordAuthor | Object classification | - |
dc.subject.keywordAuthor | Scattered point clouds | - |
dc.subject.keywordAuthor | Laser scanning | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordPlus | PROGRESS MEASUREMENT | - |
dc.subject.keywordPlus | EXISTING BUILDINGS | - |
dc.subject.keywordPlus | 3D | - |
dc.subject.keywordPlus | PHOTOGRAMMETRY | - |
dc.subject.keywordPlus | TRACKING | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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