Recognition of High-Risk Scenarios in Building Construction Based on Image Semantics-Reference-Cited by-同舟云学术

Recognition of High-Risk Scenarios in Building Construction Based on Image Semantics

Published:2020-07 Issue:4 Volume:34 Page:04020019
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Zhang Mingyuan¹,Zhu Mi²,Zhao Xuefeng³

Affiliation:

1. Associate Professor, Dept. of Construction Management, Dalian Univ. of Technology, Dalian 116000, China (corresponding author).

2. Postgraduate, Dept. of Construction Management, Dalian Univ. of Technology, Dalian 116000, China.

3. Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116000, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000900

Reference73 articles.

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2. Chahal K. S. and K. Dey. 2018. “A survey of modern object detection literature using deep learning.” Preprint submitted August 22 2018. https://arxiv.org/abs/1808.07256.

3. Dai J. Y. Li K. He and J. Sun. 2016. “R-FCN: Object detection via region-based fully convolutional networks.” In Proc. Advances in Neural Information Processing Systems 379–387. La Jolla CA: Neural Information Processing Systems.

4. Construction risk knowledge management in BIM using ontology and semantic web technology

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