Pendeteksi Masker pada Gambar Menggunakan Model Deep Learning Yolo-v2 dengan ResNet-50
Abstract
Abstrak— Sistem deteksi masker merupakan suatu upaya untuk mencegah penyebaran COVID-19. Pada penelitian ini sistem deteksi masker dikembangkan menggunakan model deep learning Yolo-v2 dengan bantuan ResNet-50. ResNet-50 digunakan sebagai backbone layer pengganti Yolo-v2, sedangkan Yolo-v2 menjadi komponen utama pendeteksi face mask. Penelitian ini menggunakan Face Mask Dataset dan Medical Mask Dataset berupa citra gambar yang diambil dari kaggle. Pengujian parameter konfigurasi saat training model dilakukan dengan harapan dapat meningkatkan akurasi dan kinerja dari sistem deteksi masker. Sistem deteksi masker menggunakan metode ini mendapatkan hasil F1-Score sebesar 84%.
Kata Kunci — deteksi masker, ResNet-50, YOLO-v2, COVID-19
References
REFERENSI
https://www.who.int/indonesia/news/novelcoronavirus/qa/qa- how-is-covid-19-transmitted
(accessed Jan. 31, 2023).
World Health Organization,
context of COVID-19: interim guidance, 1
December 2020,= World Health Organization,
WHO/2019-nCoV/IPC_Masks/2020.5, 2020.
Accessed: Jan. 31, 2023. [Online]. Available:
https://apps.who.int/iris/handle/10665/337199
D. G. Lowe,
scale-invariant features,= in Proceedings of the
Seventh IEEE International Conference on
Computer Vision, Sep. 1999, vol. 2, pp. 1150-
vol.2. doi: 10.1109/ICCV.1999.790410.
D. G. Lowe,
Scale-Invariant Keypoints,= International
Journal of Computer Vision, vol. 60, no. 2, pp.
-110, Nov. 2004, doi:
1023/B:VISI.0000029664.99615.94.
R. Girshick, J. Donahue, T. Darrell, and J.
Malik,
Object Detection and Semantic Segmentation,=
presented at the Proceedings of the IEEE
Conference on Computer Vision and Pattern
Recognition, 2014, pp. 580-587. Accessed: Jan.
, 2023. [Online]. Available:
https://openaccess.thecvf.com/content_cvpr_201
/html/Girshick_Rich_Feature_Hierarchies_201
_CVPR_paper
.html
R. Girshick,
Proceedings of the IEEE International
Conference on Computer Vision, 2015, pp.
-1448. Accessed: Jan. 31, 2023. [Online].
Available:
https://openaccess.thecvf.com/content_iccv_201
/html/Girshick_Fast_RCNN_ICCV_2015_paper.html
S. Ren, K. He, R. Girshick, and J. Sun,
R-CNN: Towards Real-Time Object Detection
with Region Proposal Networks,= in Advances
in Neural Information Processing Systems,
, vol. 28. Accessed: Jan. 31, 2023.
[Online]. Available:
https://proceedings.neurips.cc/paper/2015/hash/
bfa6bb14875e45bba028a21ed38046-
Abstract.html
J. Redmon, S. Divvala, R. Girshick, and A.
Farhadi,
Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition,
, pp. 779-788. Accessed: Jan. 31, 2023.
[Online]. Available: https://www.cvfoundation.org/openaccess/content_cvpr_2016/h
tml/Redmon_You_Only_Look_CVPR_2016_pa
per.html
J. Redmon and A. Farhadi,
Faster, Stronger,= presented at the Proceedings
of the IEEE Conference on Computer Vision
and Pattern Recognition, 2017, pp. 7263-7271.
Accessed: Jan. 31, 2023. [Online].
Available:
https://openaccess.thecvf.com/content_cvpr_201
/html/Redmon_YOLO9000_Better_Faster_CV
PR_2017_paper.html
Y. Lee, C. Lee, H.-J. Lee, and J.-S. Kim,
Detection of Objects Using a YOLOv3 Network
for a Vending Machine,= in 2019 IEEE
International Conference on Artificial
Intelligence Circuits and Systems (AICAS), Mar.
, pp. 132-136. doi:
1109/AICAS.2019.8771517.
W. Liu et al.,
Detector,= in Computer Vision - ECCV 2016,
Cham, 2016, pp. 21-37. doi: 10.1007/978-3-
-46448-0_2.
M. Loey, G. Manogaran, M. H. N. Taha, and N.
E. M. Khalifa,
novel deep learning model based on YOLO-v2
with ResNet-50 for medical face mask
detection,= Sustainable Cities and Society, vol.
, p. 102600, Feb. 2021, doi:
1016/j.scs.2020.102600.
B. Mandal, A. Okeukwu, and Y. Theis,
arXiv, Apr. 18, 2021. doi:
48550/arXiv.2104.08997. G. Kaur et al.,
model,= Neuroscience Informatics, vol. 2, no. 3,
p. 100035, Sep. 2022, doi:
1016/j.neuri.2021.100035.
