Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction
出版年份 2021 全文链接
标题
Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction
作者
关键词
Deep learning, Computer vision, Long-short term memory, PNpoly algorithm, Unsafe behaviour
出版物
ADVANCED ENGINEERING INFORMATICS
Volume 50, Issue -, Pages 101400
出版商
Elsevier BV
发表日期
2021-10-08
DOI
10.1016/j.aei.2021.101400
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Multitask Learning Method for Detecting the Visual Focus of Attention of Construction Workers
- (2021) Jiannan Cai et al. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
- Image segmentation of underfloor scenes using a mask regions convolutional neural network with two-stage transfer learning
- (2020) Gary A. Atkinson et al. AUTOMATION IN CONSTRUCTION
- Real-time smart video surveillance to manage safety: A case study of a transport mega-project
- (2020) Hanbin Luo et al. ADVANCED ENGINEERING INFORMATICS
- Robust Hybrid Approach of Vision-Based Tracking and Radio-Based Identification and Localization for 3D Tracking of Multiple Construction Workers
- (2020) Jiannan Cai et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Rework, Failures, and Unsafe Behavior: Moving Toward an Error Management Mindset in Construction
- (2020) Peter E. D. Love et al. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
- A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network
- (2019) Weili Fang et al. ADVANCED ENGINEERING INFORMATICS
- The nature and severity of workplace injuries in construction: engendering operational benchmarking
- (2019) Peter E. D. Love et al. ERGONOMICS
- End-to-end vision-based detection, tracking and activity analysis of earthmoving equipment filmed at ground level
- (2019) Dominic Roberts et al. AUTOMATION IN CONSTRUCTION
- Vision-based detection and visualization of dynamic workspaces
- (2019) Xiaochun Luo et al. AUTOMATION IN CONSTRUCTION
- Instance-level recognition and quantification for concrete surface bughole based on deep learning
- (2019) Fujia Wei et al. AUTOMATION IN CONSTRUCTION
- Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators
- (2019) Fatemeh Davoudi Kakhki et al. Applied Sciences-Basel
- Computer vision for behaviour-based safety in construction: A review and future directions
- (2019) Weili Fang et al. ADVANCED ENGINEERING INFORMATICS
- Machine learning predictive model based on national data for fatal accidents of construction workers
- (2019) Jongko Choi et al. AUTOMATION IN CONSTRUCTION
- Computer vision applications in construction safety assurance
- (2019) Weili Fang et al. AUTOMATION IN CONSTRUCTION
- Accident Analysis for Construction Safety Using Latent Class Clustering and Artificial Neural Networks
- (2019) Bilal Umut Ayhan et al. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
- Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach
- (2018) Weili Fang et al. ADVANCED ENGINEERING INFORMATICS
- Falls from heights: A computer vision-based approach for safety harness detection
- (2018) Weili Fang et al. AUTOMATION IN CONSTRUCTION
- A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory
- (2018) Lieyun Ding et al. AUTOMATION IN CONSTRUCTION
- Recognizing Diverse Construction Activities in Site Images via Relevance Networks of Construction-Related Objects Detected by Convolutional Neural Networks
- (2018) Xiaochun Luo et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Two-Dimensional Visual Tracking in Construction Scenarios: A Comparative Study
- (2018) Bo Xiao et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Reduce rework, improve safety: an empirical inquiry into the precursors to error in construction
- (2018) Peter E. D. Love et al. PRODUCTION PLANNING & CONTROL
- Unearthing the nature and interplay of quality and safety in construction projects: An empirical study
- (2018) Peter E.D. Love et al. SAFETY SCIENCE
- Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network
- (2018) Xincong Yang et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- (2017) Shaoqing Ren et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Predicting movements of onsite workers and mobile equipment for enhancing construction site safety
- (2016) Zhenhua Zhu et al. AUTOMATION IN CONSTRUCTION
- Fusion of Photogrammetry and Video Analysis for Productivity Assessment of Earthwork Processes
- (2016) M. Bügler et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Vision-Based Object-Centric Safety Assessment Using Fuzzy Inference: Monitoring Struck-By Accidents with Moving Objects
- (2016) Hongjo Kim et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Visual Tracking of Construction Jobsite Workforce and Equipment with Particle Filtering
- (2016) Zhenhua Zhu et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Predicting safety behavior in the construction industry: Development and test of an integrative model
- (2016) Brian H.W. Guo et al. SAFETY SCIENCE
- Neural Network Model for the Prediction of Safe Work Behavior in Construction Projects
- (2015) D. A. Patel et al. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
- Neural Network Model for the Prediction of Safe Work Behavior in Construction Projects
- (2015) D. A. Patel et al. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
- Robust Object Tracking via Sparse Collaborative Appearance Model
- (2014) Wei Zhong et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Visibility-related fatalities related to construction equipment
- (2011) Jimmie W. Hinze et al. SAFETY SCIENCE
- Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system
- (2010) Jochen Teizer et al. AUTOMATION IN CONSTRUCTION
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now