Safety Measure Detection Using Deep Learning

Tejas Bagthaliya; Vaidehi Shah; Shubham Shelke; Devang Shukla; Yatin Shukla

doi:10.32628/CSEIT2490216

Authors

Tejas Bagthaliya Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author
Vaidehi Shah Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author
Shubham Shelke Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author
Devang Shukla Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author
Yatin Shukla Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT2490216

Keywords:

Convolutional Neural Network , Deep Learning, Safety Measures, Object Detection, Image Classification

Abstract

This implementation is for a computer vision application that detects individuals and verifies their compliance with safety gear regulations, such as safety jackets and hard-hats. The system counts the number of individuals violating safety standards and keeps track of the total number of individuals detected. The system uses advanced image processing techniques, including object detection and classification, to accurately identify the presence or absence of safety gear. The user interface provides real-time analysis of the data, with the option to alert the user of any violations. This implementation is a valuable tool for organizations looking to ensure the safety of their employees and customers, providing a comprehensive solution for monitoring compliance with safety regulations. It can also be used to analyze trends and identify areas for improvement, making it an essential tool for safety professionals and facilities managers.

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References

K. He, X. Zhang, S. Ren and J. Sun, ”Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778, doi : 10.1109/CVPR.2016.90. DOI: https://doi.org/10.1109/CVPR.2016.90

Soltanmohammadlou, N., Sadeghi, S., Hon, C.K., Mokhtarpour-Khanghah, F. (2019). Realtime locating systems and safety in construction sites: A literature review. Safety Science. DOI: https://doi.org/10.1016/j.ssci.2019.04.025

Naticchia , Berardo et al. “A monitoring system for real-time interference control on large construction sites.” Automation in Construction 29 (2013):148-160. DOI: https://doi.org/10.1016/j.autcon.2012.09.016

Perez, L., Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification using Deep Learning. ArXiv, abs/1712.04621.

Shijie, Jia Ping, Wang Peiyi, Jia Siping, Hu. (2017). Research on data augmentation for image classification based on convolution neural networks. 4165-4170. 10.1109/CAC.2017.8243510. DOI: https://doi.org/10.1109/CAC.2017.8243510

Barro-Torres, Santiago Fern´andez-Caram´es, Tiago P´erez-Iglesias, H´ector Escudero, Carlos. (2012). Real-Time Personal Protective Equipment Monitoring System. Computer Communications. 36. 42-50. 10.1016/j.comcom.2012.01.005. DOI: https://doi.org/10.1016/j.comcom.2012.01.005

Nath, Nipun Behzadan, Amir Paal, Stephanie. (2020). Deep learning for site safety: Realtime detection of personal protective equipment. Automation in Construction. 112. 103085. 10.1016/j.autcon.2020.103085. DOI: https://doi.org/10.1016/j.autcon.2020.103085

C¸ ınar, Z.M.; Abdussalam Nuhu, A.; Zeeshan, Q.; Korhan, O.; Asmael, M.; Safaei, B. Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability 2020, 12,8211. 32.

Lee J, Lee S. Construction Site Safety Management: A Computer Vision and Deep Learning Approach. Sensors (Basel). 2023 Jan 13;23(2):944. doi: 10.3390/s23020944. PMID: 36679738; PMCID: PMC9863726.

Le, Billy Phuc Jeon, HyeJun Truong, Nguyen Hak, Jung. (2019). Applying the Haarcascade Algorithm for Detecting Safety Equipment in Safety Management Systems for Multiple Working Environments. Electronics. 8. 1079. 10.3390/electronics8101079.

Cinar, Zeki Nuhu, Abubakar Zeeshan, Qasim Korhan, Orhan Asmael, Mohammed Safaei, Babak. (2020). Machine Learning in Predictive Maintenance towards Sustainable SmartManufacturing in Industry 4.0. Sustainability. 12. 8211. 10.3390/su12198211. DOI: https://doi.org/10.3390/su12198211

Lee J, Lee S. Construction Site Safety Management: A Computer Vision and Deep Learning Approach. Sensors (Basel). 2023 Jan 13;23(2):944. doi: 10.3390/s23020944. PMID: 36679738; PMCID:PMC9863726. DOI: https://doi.org/10.3390/s23020944

X. Wang, D. Niu, P. Luo, C. Zhu, L. Ding and K. Huang, ”A Safety Helmet and Protective Clothing Detection Method based on Improved-Yolo V 3,” 2020 Chinese Automation Congress (CAC), Shanghai China, 2020, pp. 5437-5441, doi:10.1109/CAC51589.2020.9327187. DOI: https://doi.org/10.1109/CAC51589.2020.9327187

Ravikiran, Manikandan. (2019). Improving Industrial Safety Gear Detection through Re-ID conditioned Detector. DOI: https://doi.org/10.1109/AIPR47015.2019.9174597

le, Billy Phuc Jeon, HyeJun Truong, Nguyen Hak, Jung. (2019). Applying the Haar-cascade Algorithm for Detecting Safety Equipment in Safety Management Systems for Multiple Working Environments. Electronics. 8. 1079. 10.3390/electronics8101079. DOI: https://doi.org/10.3390/electronics8101079