Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach
By Weili Fang, L. Ding, B. Zhong, Peter E. D. Love, Hanbin Luo. Advanced Engineering Informatics, 2018
323 citations recorded by the source corpus.
Abstract Detecting the presence of workers, plant, equipment, and materials (i.e. objects) on sites to improve safety and productivity has formed an integral part of computer vision-based research in construction. Such research has tended to focus on the use of computer vision and pattern recognition approaches that are overly reliant on the manual extraction of features and small datasets (
Subfields: Civil and Structural Engineering, Geology, Radiological and Ultrasound Technology
View Paper: https://www.semanticscholar.org/paper/e5aece1b24df3841e503e7326590a0d7f3d9b71f
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In Civil and Structural Engineering
Citations in the corpus
- Construction with digital twin information systems (2020, background): “Some notable exceptions prove the rule: for example, some collision alert systems employ separate technologies to locate heavy machinery (e.g., GPS) and workers (e.g., computer vision), merging the data to generate actionable information (Seo et al., 2015; Fang et al., 2018).”