Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management

Abstract

-Recognizing construction assets from as-is point cloud data of construction environment provides essential information for engineering and management applications including progress monitoring, safety management, supply-chain management, and quality control. This study proposes a fast and automated processing pipeline for construction target assets recognition from scattered as-is point clouds. The recognition tasks can be subdivided into object detection, which involves computing the bounding box around each construction equipment, and object classification, which involves labelling point cloud clusters from discrete equipment categories, such as backhoe loader, bulldozer, dump truck, excavator and front loader. The object detection step consists of point cloud down-sampling, segmentation and clustering. For the object classification step, machine learning methods were employed to determine class membership probability using features derived from the ESF (Ensemble of Shape Functions) descriptor. The classifiers were trained on synthetic point clouds generated from CAD (Computer-aided Design) models. The method was validated using laser scanned point clouds from an equipment yard. The test results demonstrate promising advancements towards semantic labelling and scene understanding of point cloud data.

A scientometric review of construction progress monitoring studies

“…Intra-cluster linkage of automation with construction equipment and labour performance depicts that automation techniques have been extensively researched to recognize the construction activity (Chen et al , 2019; Liu and Golparvar-Fard, 2015), construction equipment tracking (Chen et al , 2016) and labour productivity analysis (Chen et al , 2017).…”

New Automated BIM Object Classification Method to Support BIM Interoperability

“…Also working on point cloud data but from a different perspective, Voegtle and Steinle (2003) designed a method to detect segments and extract objects inside these segments based on a special region growing algorithm. LIDAR data has been widely used in the civil engineering domain for capturing as-built projects (Wang and Cho 2014), prefabricated components (Kalasapudi et al 2015), construction equipment and assets (Chen et al 2016;Fang et al 2016), and surveying results (Tang and Akinci 2012 proposed a hierarchical matching pursuit (HMP) method for RGB-D data object classification, which uses an unsupervised learning technique with sparse coding to generate hierarchical feature representations for classifying household objects.…”