Evaluation of computer vision techniques for automated hardhat detection in indoor construction safety applications

Bahaa Eddine MNEYMNEH, Mohamad ABBAS, Hiam KHOURY

PDF(2225 KB)
PDF(2225 KB)
Front. Eng ›› 2018, Vol. 5 ›› Issue (2) : 227-239. DOI: 10.15302/J-FEM-2018071
RESEARCH ARTICLE

Evaluation of computer vision techniques for automated hardhat detection in indoor construction safety applications

Author information +
History +

Abstract

Construction is considered among the most dangerous industries and is responsible for a large portion of total worker fatalities. A construction worker has a probability of 1-in-200 of dying on the job during a 45-year career, mainly due to fires, falls, and being struck by or caught between objects. Hence, employers must ensure their workers wear personal protective equipment (PPE), in particular hardhats, if they are at risk of falling, being struck by falling objects, hitting their heads on static objects, or coming in proximity to electrical hazards. However, monitoring the presence and proper use of hardhats becomes inefficient when safety officers must survey large areas and a considerable number of workers. Using images captured from indoor jobsites, this paper evaluates existing computer vision techniques, namely object detection and color-based segmentation tools, used to rapidly detect if workers are wearing hardhats. Experiments are conducted and the results highlight the potential of cascade classifiers, in particular, to accurately, precisely, and rapidly detect hardhats under different scenarios and for repetitive runs, and the potential of color-based segmentation to eliminate false detections.

Keywords

construction / safety / personal protective equipment / hardhat / computer vision

Cite this article

Download citation ▾
Bahaa Eddine MNEYMNEH, Mohamad ABBAS, Hiam KHOURY. Evaluation of computer vision techniques for automated hardhat detection in indoor construction safety applications. Front. Eng, 2018, 5(2): 227‒239 https://doi.org/10.15302/J-FEM-2018071

References

[1]
Abbas M, Mneymneh B E, Khoury H (2016). Use of unmanned aerial vehicles and computer vision in construction safety inspections. In: Proceedings of the 3rd Australasia and South-East Asia Structural Engineering and Construction Conference (ASEA-SEC-3). Kuching, Sarawak, Malaysia
CrossRef Google scholar
[2]
Abbas M, Mneymneh B E, Khoury H (2018). Assessing on-site construction personnel hazard perception in a middle eastern developing country: An interactive graphical approach. Safety Science Journal, Elsevier Science, 103: 183–196
CrossRef Google scholar
[3]
Alionte E, Lazar C (2015). A practical implementation of face detection by using matlab cascade object detector. In: Proceedings of Conference on System Theory, Control and Computing. Cheile Gradistei, Romania, 785–790
[4]
Bajracharya D (2013). Real time pattern recognition in digital video with application to safety in construction sites. Dissertation for the College Degree. Las Vegas: University of Nevada–Las Vegas
[5]
Brunelli R (2009). Template Matching Techniques in Computer Vision: Theory and Practice. New Jersey: Wiley
[6]
Chdid D, Oueis R, Khoury H, Asmar D, Elhajj I (2011). Inertial-vision sensor fusion for pedestrian localization. In: Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO). 1695–1701
[7]
Cheng T, Teizer J (2014). Modeling tower crane operator visibility to minimize the risk of limited situational awareness. Journal of Computing in Civil Engineering, 28(3): 04014004
CrossRef Google scholar
[8]
Chi S, Caldas C H, Kim D Y (2009). A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques. Computer-Aided Civil and Infrastructure Engineering, 24(3): 199–211
CrossRef Google scholar
[9]
Cyganek B (2013). Object Detection and Recognition in Digital Images: Theory and Practice. New Jersey: Wiley
[10]
Dickscheid T, Schindler F, Förstner W (2011). Coding images with local features. International Journal of Computer Vision, 94(2): 154–174
CrossRef Google scholar
[11]
Dimitrov A, Golparvar-Fard M (2014). Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections. Advanced Engineering Informatics, Elsevier, 28(1): 37–49
CrossRef Google scholar
[12]
Ding L Y, Yu H L, Li H, Zhou C, Wu X G, Yu M H (2012). Safety risk identification system for metro construction on the basis of construction drawings. Automation in Construction, Elsevier, 27: 120–137
CrossRef Google scholar
[13]
Du S, Shehata M, Badawy W (2011). Hardhat detection in video sequences based on face features, motion and color information. In: Proceedings of 3rd IEEE International Conference on Computer Research and Development
CrossRef Google scholar
[14]
Fang Q, Li H, Luo X, Ding L, Luo H, Rose T M, An W (2018). Detecting non-hardhat-use by a deep learning method from far-field surveillance videos. Automation in Construction, 85: 1–9
CrossRef Google scholar
[15]
Gheisari M, Irizarry J, Walker B N (2014). UAS4SAFETY: The potential of unmanned aerial systems for construction safety applications. Construction Research Congress
CrossRef Google scholar
[16]
Girshick R (2015). Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision. 1440–1448
[17]
Gualdi G, Prati A, Cucchiara R (2011). Contextual Information and covariance descriptors for people surveillance: An application for safety of construction workers. EURASIP Journal on Image and Video Processing, 2011(1): 1–16
CrossRef Google scholar
[18]
Ham Y, Han K K, Lin J J, Golparvar-Fard M (2016). Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): A review of related works. Visualization in Engineering, 4(1): 1
CrossRef Google scholar
[19]
Hamledari H, McCabe B, Davari S (2017). Automated computer vision-based detection of components of under construction indoor partition. Automation in Construction, Elsevier, 74: 78–94
CrossRef Google scholar
[20]
He K, Zhang X, Ren S, Sun J (2014). Spatial pyramid pooling in deep convolutional networks for visual recognition. European Conference on Computer Vision, 346–361
[21]
Health and Safety Executive (2014). Health and safety in construction sector in Great Britain. http://www.hse.gov.uk/statistics/industry/construction/construction.pdf
[22]
Kaur N, Banga V K, Kaur A (2013). Image segmentation based on color. In: Proceedings of IJRET. International Journal of Research in Engineering and Technology, 2(11)
CrossRef Google scholar
[23]
Khoury H, Chdid D, Oueis R, Elhajj I, Asmar D (2015). Infrastructureless approach for ubiquitous user location tracking in construction environments. Automation in Construction, 56: 47–66
CrossRef Google scholar
[24]
Khoury H M, Akula M, Kamat V R (2012). Mobile and Pervasive Computing in Construction. Hoboken: John Wiley & Sons
[25]
Khoury H M, Kamat V R (2009a). Evaluation of position tracking technologies for user localization in indoor construction environments. Automation in Construction, Elsevier Science, 18(4): 444–457
CrossRef Google scholar
[26]
Khoury H M, Kamat V R (2009b). High-precision identification of contextual information in location-aware engineering applications. Advanced Engineering Informatics, Elsevier Science, 23(4): 483–496
CrossRef Google scholar
[27]
Kim H, Lee Y, Yim B, Park E, Kim H (2016). On-road object detection using deep neural network. In: Proceedings of IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 1–4
[28]
Kim Y S, Lee J H, Yoo H S, Lee J B, Jung U S (2009). A performance evaluation of a Stewart platform based Hume concrete pipe manipulator. Automation in Construction, 18(5): 665–676
CrossRef Google scholar
[29]
Kopp A P, Saidi K S, Khoury H M (2010). Evaluation of ultra-wideband technology for use in 3D locating systems. In: Proceedings of the 10th Performance Metrics for Intelligent Systems (PerMIS) Workshop. Baltimore, MD, United States, 7–12
[30]
Krizhevsky A, Sutskever I, Hinton G E (2012). Imagenet classification with deep convolutional neural networks. In: Proceedings of the International Conference on Neural Information Processing Systems. 1097–1105
[31]
Memarzadeh M, Golparvar-Fard M, Niebles J C (2013). Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors. Automation in Construction, 32: 24–37
CrossRef Google scholar
[32]
Mikolajczyk K, Tuytelaars T (2009). Encyclopedia of Biometrics. New York: Springer US
[33]
Mneymneh B E, Abbas M, Khoury H (2016). A UAV-based image processing system for identifying and visualizing construction hazardous areas. In: Proceedings of 16th International Conference on Construction Applications of Virtual Reality. Hong Kong Occupational Safety and Health Administration
[34]
Commonly Used Statistics. https://www.osha.gov/oshstats/commonstats.html, 2016–12–19
[35]
Oueiss R, Chdid D, Khoury H M, Asmar D, Elhajj I (2012). Infrastructureless inspection process automation in construction. International Society for Gerontechnology, 11(2): 92–98
[36]
Park MW, Elsafty N, Zhu Z (2015). Hardhat-wearing detection for enhancing on-site safety of construction workers. Journal of Construction Engineering and Management, 141(9)
CrossRef Google scholar
[37]
Park M W, Koch C, Brilakis I (2012). Three-dimensional tracking of construction resources using an on-site camera system. Journal of Computing in Civil Engineering, 26(4): 541–549
CrossRef Google scholar
[38]
Peng Q, Luo W, Hong G, Feng M, Xia Y, Yu L, Hao X, Wang X, Li M (2016). Pedestrian detection for transformer substation based on gaussian mixture model and YOLO. In: Proceedings of International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 562–565
[39]
Ren S, He K, Girshick R, Sun J (2015). Faster r-cnn: towards real-time object detection with region proposal networks. In: Proceedings of International Conference on Neural Information Processing Systems. 91–99
[40]
Rubaiyat A H M, Toma T T, Kalantari-Khandani M, Rahman S A, Chen L, Ye Y, Pan C S (2016). Automatic detection of helmet uses for construction safety. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence Workshops. 135–142
[41]
Seo J, Han S, Lee S, Kim H (2015). Computer vision techniques for construction safety and health monitoring. Advanced Engineering Informatics, Elsevier, 29(2): 239–251
CrossRef Google scholar
[42]
Shrestha K, Sherestha P P, Bajracharya D, Yfantis E A (2015). Hard-hat detection for construction safety visualization. Journal of Construction Engineering, 1–8
[43]
Skibniewski M J (2014). Information technology applications in construction safety assurance. Journal of Civil Engineering and Management, 20(6): 778–794
CrossRef Google scholar
[44]
Chae S,Yoshida T, (2010). Application of RFID technology to prevention of collision accident with heavy equipment. Automation in Construction, 19(3): 368–374
CrossRef Google scholar
[45]
Yu J, Amores J, Sebe N, Tian Q (2006). A new study on distance metrics as similarity measurement. In: Proceedings of IEEE International Conference on Multimedia and Expo
[46]
Zhou Y, Nejati H, Do T T, Cheung N M, Cheah L (2016). Image-based vehicle analysis using deep neural network: A systematic study. In: Proceedings of IEEE International Conference on Digital Signal Processing

Acknowledgments

The presented work was supported by AUB’s University Research Board (URB). The authors gratefully acknowledge URB support. Any opinions, findings, conclusions, and recommendations expressed by the authors in this paper do not necessarily reflect the views of URB.

RIGHTS & PERMISSIONS

2018 The Author(s) 2018. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
AI Summary AI Mindmap
PDF(2225 KB)

Accesses

Citations

Detail

Sections
Recommended

/