Abstract
Wearing a helmet is mandatory for workers at construction sites. It is very important for the safety of workers during work. In many scenarios, detecting workers not wearing helmets can prevent possible occupational accidents in time. Recently, with the rapid development of deep learning, convolutional neural networks (CNNs) have been widely applied in many problems including object detection. The constantly evolving object detection technology has resulted in a series of YOLO algorithms with very high accuracy and speed being used in various scene detection tasks. This paper presents a deep learning approach to solve the above problems. We propose a helmet detection method based on two models, namely YOLOv5 and YOLOR, using a dataset of 900 collected images. The two models are compared and analyzed. The experimental results show that the mAP@0.5 of YOLOR reached 87.3%, significantly larger than that of the YOLOv5 model with mAP@0.5 of only 77.6%, proving the effectiveness of helmet detection using the YOLOR model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Narayanan, A., Prabhavalkar, R., Chiu, C.C., Rybach, D., Sainath, T.N., Strohman, T.: Recognizing long-form speech using streaming end-to-end models. In: 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 920–927. IEEE, December 2019
Shaqra, F.A., Duwairi, R., Al-Ayyoub, M.: Recognizing emotion from speech based on age and gender using hierarchical models. Procedia Comput. Sci. 151, 37–44 (2019)
Ho, T.T., Kim, G.T., Kim, T., Choi, S., Park, E.K.: Classification of rotator cuff tears in ultrasound images using deep learning models. Med. Biol. Eng. Comput. 60, 1269–1278 (2022). https://doi.org/10.1007/s11517-022-02502-6
Ho, T.T., et al.: A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects. Sci. Rep. 11(1), 1–12 (2021)
Park, S.S., Tran, V.T., Doan, N.P., Hwang, K.B.: Evaluation of damage level for ground settlement using the convolutional neural network. In: Ha-Minh, C., Tang, A.M., Bui, T.Q., Vu, X.H., Huynh, D.V.K. (eds.) CIGOS 2021. LNCE, vol. 203, pp. 1261–1268. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7160-9_128
Park, S.S., Tran, V.T., Lee, D.E.: Application of various YOLO models for computer vision-based real-time pothole detection. Appl. Sci. 11(23), 11229 (2021)
Dinh, V.Q., Munir, F., Azam, S., Yow, K.C., Jeon, M.: Transfer learning for vehicle detection using two cameras with different focal lengths. Inf. Sci. 514, 71–87 (2020)
Ho, T.T., et al.: Deep learning models for predicting severe progression in COVID-19-infected patients: retrospective study. JMIR Med. Inform. 9(1), e24973 (2021)
Do, D.T., Lee, J., Nguyen-Xuan, H.: Fast evaluation of crack growth path using time series forecasting. Eng. Fract. Mech. 218, 106567 (2019)
Nguyen, D.L.H., Do, D.T.T., Lee, J., Rabczuk, T., Nguyen-Xuan, H.: Forecasting damage mechanics by deep learning. CMC-Comput. Mater. Continua 61(3), 951–977 (2019)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Wang, C.Y., Yeh, I.H., Liao, H.Y.M.: You only learn one representation: unified network for multiple tasks. arXiv preprint arXiv:2105.04206 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tran, V.T., To, T.S., Nguyen, TN., Tran, T.D. (2022). Safety Helmet Detection at Construction Sites Using YOLOv5 and YOLOR. In: Nguyen, NT., Dao, NN., Pham, QD., Le, H.A. (eds) Intelligence of Things: Technologies and Applications . ICIT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 148. Springer, Cham. https://doi.org/10.1007/978-3-031-15063-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-15063-0_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15062-3
Online ISBN: 978-3-031-15063-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)