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Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach
Affiliation:1. School of Civil Engineering &Mechanics, Huazhong University of Science & Technology, Wuhan, China;2. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong;3. School of Civil Engineering and Built Environment, Queensland University of Technology, Australia;4. Department of Computing, The Hong Kong Polytechnic University, Hong Kong;1. Dept. of Construction Management, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, Hubei, China;2. Hubei Engineering Research Center for Virtual, Safe and Automated Construction, China;3. Dept. of Civil Engineering, Curtin University, Perth, Western Australia, Australia;4. School of Electronic Information and communications, Huazhong University of Science and Technology, Wuhan, Hubei, China
Abstract:
Keywords:Deep learning  Image  Improved Faster R-CNN  Object detection  Construction site
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