Safety Helmet Detection on Field Project Worker Using Detection Transformer
Abstract
There have been many cases of work accidents caused by not complying with safety standards at work, especially in the use of safety helmets. This study is able to make regular observations in identifying project personnel using safety helmets at work, this aims to reduce the risk of accidents at work, namely in the use of helmet attributes at work. Some previous studies, have proposed the use of image detection-based models using the Detection Transformer (DeTr) method for obtaining object detection, group prediction, and combining methods, using the Intersection over Union (IoU) method for obtaining object detection results, to achieve the best performance, namely to get convergence results. Based on the combination of these two methods, the results value of average IoU is 0.50 from 500 identified project personnel data were obtained.
Downloads
References
T. A. A. H. Kusuma, K. Usman, and S. Saidah, “People Counting For Public Transportations Using You Only Look Once Method,” Jurnal Teknik Informatika (Jutif), vol. 2, no. 1, pp. 57–66, Feb. 2021, doi: 10.20884/1.jutif.2021.2.2.77.
M. Zhong and F. Meng, “A YOLOv3-based non-helmet-use detection for seafarer safety aboard merchant ships,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Nov. 2019. doi: 10.1088/1742-6596/1325/1/012096.
H. Wang, Z. Hu, Y. Guo, Z. Yang, F. Zhou, and P. Xu, “A real-time safety helmet wearing detection approach based on csyolov3,” Applied Sciences (Switzerland), vol. 10, no. 19, pp. 1–14, 2020, doi: 10.3390/app10196732.
A. Hayat and F. Morgado-Dias, “Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety,” Applied Sciences (Switzerland), vol. 12, no. 16, 2022, doi: 10.3390/app12168268.
M. Lin et al., “DETR for Crowd Pedestrian Detection,” 2020, [Online]. Available: http://arxiv.org/abs/2012.06785
R. M. Mailoa and L. W. Santoso, “Deteksi Rompi dan Helm Keselamatan Menggunakan Metode YOLO dan CNN,” Jurnal Infra, vol. 10, no. 2, pp. 56–62, 2022.
S. B. Setyawan, W. Pribadi, H. Arrosida, and E. P. Nugroho, “Sistem Deteksi Pengendara Sepeda Motor Tanpa Helmdan Kelebihan Penumpang pada Dengan Menggunakan YOLO V3,” Seminar Nasional Terapan Riset Inovatif, vol. 7,no. 1, pp. 430–438, 2021.
R, M. “Survey on image preprocessing techniques to improve OCR accuracy,” medium.com, 2021. [Online] Available: https://medium.com/technovators/survey-on-image-preprocessing-techniques-to-improve-ocr-accuracy-616ddb931b76.
Z. Sun, S. Cao, Y. Yang, and K. M. Kitani, “Rethinking Transformer-based Set Prediction for Object Detection,” Oct. 2021, doi: https://doi.org/10.1109/iccv48922.2021.00359.
Dedhia, P.R “Faster R-CNN: A step towards real-time object detection,” medium.com, 2020. [Online] Available: https://towardsdatascience.com/faster-r-cnn-a-step-towards-real-time-object-detection-98c186732a69.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-End Object Detection with Transformers,” May 2020, [Online]. Available: http://arxiv.org/abs/2005.12872
Dai Zhigang, B. Cai, Y. Lin, and J. Chen, “UP-DETR: Unsupervised Pre-training for Object Detection with Transformers,” Computer Vision and Pattern Recognition, Jun. 2021, doi: https://doi.org/10.1109/cvpr46437.2021.00165.
Y. Liu, B. Schiele, A. Vedaldi, and C. Rupprecht, “Continual Detection Transformer for Incremental Object Detection,” pp. 23799–23808, 2023.
Hila Chefer, S. Gur, and L. Wolf, “Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers,” Oct. 2021, doi: https://doi.org/10.1109/iccv48922.2021.00045.
S.-H. Tsang, “Review — DETR: End-to-End Object Detection with Transformers,” towardsdatascience.com, 2022. [Online]. Available: https://sh-tsang.medium.com/review-detr-end-to-end-object-detection-with-transformers-c64977be4b8e.
Yu, G., Xiang, N. and Pan, C. “Pedestrian detection in crowded scenes based on Cascade R-CNN” 2022 8th International Conference on Computer Technology Applications. doi:10.1145/3543712.3543720.
K. Kim and H. S. Lee, “Probabilistic Anchor Assignment with IoU Prediction for Object Detection,” Jul. 2020, [Online]. Available: http://arxiv.org/abs/2007.08103
W. Rahmaniar and A. Hernawan, “Real-time human detection using deep learning on embedded platforms: A review,” Journal of Robotics and Control (JRC), vol. 2, no. 6. Department of Agribusiness, Universitas Muhammadiyah Yogyakarta, pp. 462-468Y, Nov. 01, 2021. doi: 10.18196/jrc.26123.
H. Rezatofighi et al., “Generalized intersection over union: A metric and a loss for bounding box regression,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. doi:10.1109/cvpr.2019.00075
G. Zhang, L. Ge, Y. Yang, Y. Liu, and K. Sun, “Fused Confidence for Scene Text Detection via Intersection-over-Union,” Oct. 2019, doi: https://doi.org/10.1109/icct46805.2019.8947307.
R. Padilla, S. L. Netto, and E. A. B. da Silva, “A Survey on Performance Metrics for Object-Detection Algorithms,” 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Jul. 2020, doi: https://doi.org/10.1109/iwssip48289.2020.9145130.
Larxel, “Safety helmet detection,” Kaggle.com, 2020. [Online]. Available at: https://ww w.kaggle.com/datasets/andrewmvd/hard-hat-detection
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Safety Helmet Detection on Field Project Worker Using Detection Transformer
Pages: 1316-1323
Copyright (c) 2023 Muhammad Rayhan Subhi, Ema Rachmawati, Gamma Kosala

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).






















