Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds
- Authors
- Chen, Jingdao; Fang, Yihai; Cho, Yong K.; Kim, Changwan
- Issue Date
- Mar-2017
- Publisher
- ASCE-AMER SOC CIVIL ENGINEERS
- Keywords
- Object recognition; Object classification; Scattered point clouds; Laser scanning; Machine learning
- Citation
- JOURNAL OF COMPUTING IN CIVIL ENGINEERING, v.31, no.2
- Journal Title
- JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Volume
- 31
- Number
- 2
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/4750
- DOI
- 10.1061/(ASCE)CP.1943-5487.0000628
- ISSN
- 0887-3801
1943-5487
- 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.
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