Improved detection network model based on YOLOv5 for warning safety in construction sites

[1]  Zhenghai Lu,et al.  Helmet Wearing State Detection Based on Improved Yolov5s , 2022, Sensors.

[2]  M. Skitmore,et al.  Advanced technologies for enhanced construction safety management: investigating Malaysian perspectives , 2022, International Journal of Construction Management.

[3]  Chaoqun Duan,et al.  A lightweight vehicles detection network model based on YOLOv5 , 2022, Eng. Appl. Artif. Intell..

[4]  Hanbin Luo,et al.  Deep learning-based data analytics for safety in construction , 2022, Automation in Construction.

[5]  J. Gu,et al.  A lightweight YOLOv3 algorithm used for safety helmet detection , 2022, Scientific Reports.

[6]  Xuliang Duan,et al.  Research on the algorithm of helmet-wearing detection based on the optimized yolov4 , 2022, The Visual Computer.

[7]  Om Pal,et al.  FD-YOLOv5: A Fuzzy Image Enhancement Based Robust Object Detection Model for Safety Helmet Detection , 2022, International Journal of Fuzzy Systems.

[8]  M. Palaniswami,et al.  Real-time monitoring of construction sites: Sensors, methods, and applications , 2022, Automation in Construction.

[9]  Hui Deng,et al.  A semantic framework for on-site evacuation routing based on awareness of obstacle accessibility , 2022, Automation in Construction.

[10]  Y. Li,et al.  An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease , 2022, Comput. Electron. Agric..

[11]  Qiu Chen,et al.  An improved feature pyramid network for object detection , 2022, Neurocomputing.

[12]  Idris Jeelani,et al.  Computer vision applications in construction: Current state, opportunities & challenges , 2021, Automation in Construction.

[13]  Numan Khan,et al.  Rigorous analysis of safety rules for vision intelligence-based monitoring at construction jobsites , 2021, International Journal of Construction Management.

[14]  Shilpa Sharma,et al.  Authentication control system for the efficient detection of hard-hats using deep learning algorithms , 2021, Journal of Discrete Mathematical Sciences and Cryptography.

[15]  Venkata Santosh Kumar Delhi,et al.  Review of construction safety performance measurement methods and practices: a science mapping approach , 2021, International Journal of Construction Management.

[16]  Patrick X.W. Zou,et al.  Computer vision technologies for safety science and management in construction: A critical review and future research directions , 2021 .

[17]  Tarek Zayed,et al.  IoT-based application for construction site safety monitoring , 2020, International Journal of Construction Management.

[18]  Zhile Yang,et al.  A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3 , 2020, Applied Sciences.

[19]  David J. Edwards,et al.  A bibliometric review of the status and emerging research trends in construction safety management technologies , 2020, International Journal of Construction Management.

[20]  Amir H. Behzadan,et al.  Deep learning for site safety: Real-time detection of personal protective equipment , 2020 .

[21]  Peter E.D. Love,et al.  Computer vision for behaviour-based safety in construction: A review and future directions , 2020, Adv. Eng. Informatics.

[22]  Amir H. Behzadan,et al.  Single- and multi-label classification of construction objects using deep transfer learning methods , 2019, J. Inf. Technol. Constr..

[23]  Jixiu Wu,et al.  Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset , 2019, Automation in Construction.

[24]  Dimitris N. Metaxas,et al.  ASSD: Attentive Single Shot Multibox Detector , 2019, Comput. Vis. Image Underst..

[25]  Hao Wu,et al.  An intelligent vision-based approach for helmet identification for work safety , 2018, Comput. Ind..

[26]  Xiaowei Luo,et al.  Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images , 2018 .

[27]  Peter E.D. Love,et al.  A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory , 2018 .

[28]  Xiaochun Luo,et al.  Detecting non-hardhat-use by a deep learning method from far-field surveillance videos , 2018 .

[29]  Yanfang Ye,et al.  Unsupervised Feature Learning for Objects of Interest Detection in Cluttered Construction Roof Site Images , 2016 .

[30]  Heng Li,et al.  Automated PPE Misuse Identification and Assessment for Safety Performance Enhancement , 2015 .

[31]  SangHyun Lee,et al.  Computer vision techniques for construction safety and health monitoring , 2015, Adv. Eng. Informatics.

[32]  Evangelos A. Yfantis,et al.  Hard-Hat Detection for Construction Safety Visualization , 2015 .

[33]  Jochen Teizer,et al.  Mobile passive Radio Frequency Identification (RFID) portal for automated and rapid control of Personal Protective Equipment (PPE) on construction sites , 2013 .

[34]  Berardo Naticchia,et al.  A monitoring system for real-time interference control on large construction sites , 2013 .