Monitoring cameras for violations in the use of personal protective equipment
DOI:
https://doi.org/10.24036/k6kjzn25Keywords:
Work Safety , Protective Equipment , camera , monitoring, GUIAbstract
Work Safety is an activity and effort to create a safe work environment, preventing all forms of accidents. To implement safe actions for workers, Personal Protective Equipment (PPE) is needed for workers to protect someone in doing their work. This Personal Protective Equipment (PPE) monitoring system begins by turning on the system by flowing the power supply to the system. Then the system is set via an access point via WiFi to be able to set the system on the microcontroller GUI. Then by selecting to activate "Motion Detect" and "Auto Upload" on the GUI, the system will automatically detect all movements within the range of the camera sensor. Furthermore, the system saves the video image of the object's movement in the memory on the system and will then be uploaded to a personal computer that has been set as an FTP server. After the personal computer saves the video image, the personal computer then sends the video image to the roboflow platform server. And when the video image is sent to the roboflow platform server, the video image is classified and labeled according to the dataset that the author created in the previous python programming. And finally, the video image that has been classified and labeled is stored again in the directory that has been specified in the programming
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References
[1] A. A. Protik, A. H. Rafi and S. Siddique, "Real-time Personal Protective Equipment (PPE) Detection Using YOLOv4 and TensorFlow", 2021 IEEE Region 10 Symposium (TENSYMP), pp. 1-6, 2021.
[2] M. L. R. Collo, J. Richard, M. Esguerra, R. V. Sevilla, J. Merin and D. C. Malunao, "A COVID-19 Safety Monitoring System: Per-sonal Protective Equipment (PPE) Detection using Deep Learning", 2022 International Conference on Decision Aid Sciences and Ap-plications (DASA), pp. 295-299, 2022.
[3] T. Q. Vinh and N. T. N. Anh, "Real-Time Face Mask Detector Using YOLOv3 Algorithm and Haar Cascade Classifier", 2020 International Conference on Advanced Computing and Applications (ACOMP), pp. 146-149, 2020.
[4] J. Lo, L. Lin and K. & C. Hung, "Real-time personal protective equipment compliance detection based on deep learning algorithm", Sustainability, vol. 15, no. 1, pp. 391, 2022.
[5] Kemennakertrans, “Peraturan Menteri Tenaga Kerja dan Transmigrasi Republik Indonesia Nomor PER.08/MEN/VII/2010 Tentang Alat Pelindung Diri,” Peraturan Menteri tenaga Kerja dan Transmigrasi, vol. VII, no. 8, pp. 1–69, 2010.
[6] T. A. Dompeipen and S. R. U. . Sompie, “Penerapan computer vision untuk pendeteksian dan penghitung jumlah manusia,” J. Tek. Inform., vol. 15, no. 4, pp. 1–12, 2020.
[7] N. Abidin, “Optimalisasi Firewall Pada Jaringan Komputer Berskala Luas,” J. Sist. Inf., vol. Volume 1, no. 1, pp. 84–94, 2019.
[8] A. Archana, Jennifer Gladius, A. Meriton and Paul Christina, "A study on personal protective equipment use among health care providers Tamil Nadu", International Journal Of Community Medicine And Public Health, 2018.
[9] J. Ofonime and M. Olugbemi, "Knowledge and Use of Personal Protective Equipment among Auto Technicians in Uyo", Nigeria. British Journal of Education Society Behavioural Science, vol. 15, pp. 1-8, 2016.
[10] X. Yang, Y. Yu, S. Shirowzhan, S. Sepasgozar and H. Li, "Automated ppe-tool pair check system for construction safety using smart iot", Journal of Building Engineering, vol. 32, pp. 101721, 2020.
[11] S. Márquez-Sánchez, I. Campero-Jurado, J. Herrera-Santos, S. Rodríguez and J. M. Corchado, "Intelligent platform based on smart ppe for safety in workplaces", Sensors, vol. 21, no. 14, 2021.
[12] G. Gallo, F. Di Rienzo, P. Ducange, V. Ferrari, A. Tognetti and C. Vallati, "A smart system for personal protective equipment detection in industrial environments based on deep learning", 2021 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 222-227, 2021.
[13] X. Ke, W. Chen and W. Guo, "100+ fps detector of personal protective equipment for worker safety: A deep learning approach for green edge computing", Peer-to-peer networking and applications, vol. 15, no. 2, pp. 950-972, 2022.
[14] S. Chen and K. Demachi, "A vision-based approach for ensuring proper use of personal protective equipment (ppe) in decommissioning of fukushima daiichi nuclear power station", Applied Sciences, vol. 10, no. 15, 2020.
[15] A. M. Vukicevic, M. Djapan, V. Isailovic, D. Milasinovic, M. Savkovic and P. Milosevic, "Generic compliance of industrial ppe by using deep learning techniques", Safety Science, vol. 148, pp. 105646, 2022.
