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Abstract
Deteksi potongan tubuh manusia adalah tugas yang sangat menantang. Deteksi potongan tubuh manusia dapat diterapkan dalam berbagai situasi seperti pencarian korban bencana alam, pencarian potongan tubuh yang tertutup dengan objek lain, korban kecelakaan, dan lain – lain sehingga dapat memudahkan para pengguna untuk mendeteksi potongan tubuh manusia. Potongan tubuh dalam penelitian ini dibedakan dalam dua kategori yaitu berdasarkan kelengkapan potongan tubuh manusia dan kelas yang dideteksi. Tujuan pada penelitian ini adalah untuk mengetahui kinerja Faster R-CNN dengan ResNet-101 dalam mendeteksi potongan tubuh manusia. Jumlah dataset yang digunakan sebanyak 100 citra gambar. Berdasarkan hasil penelitian, Faster R-CNN dengan ResNet-101 pada step 1000, 2000, dan 3000 menunjukkan bahwa hasil terbaik pada deteksi step 3000 dengan Precision 79,50%, Recall 68,80%, dan F1 score 73,76%. Pada penelitian ini, pemanfaatan algoritma Faster R-CNN dengan ResNet-101 mampu mendeteksi potongan tubuh Manusia namun kualitas piksel dan ketajaman pada citra gambar juga mempengaruhi hasil deteksi.
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References
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References
N. Heryana, R. Mayasari, and K. A. Baihaqi, “Penerapan Haar Cascade Classification Model Untuk Deteksi Wajah, Hidung, Mulut, dan Mata Menggunakan Algoritma Viola-Jones,” 2020.
X. Wu, D. Sahoo, and S. C. H. Hoi, “Recent advances in deep learning for object detection,” Neurocomputing, vol. 396, pp. 39–64, Jul. 2020, doi: 10.1016/j.neucom.2020.01.085.
L. K. Topham, W. Khan, ljmuacuk Dhiya Al-Jumeily, and ljmuacuk Abir Hussain, “Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models”, [Online]. Available: http://researchonline.ljmu.ac.uk/
Z. Zou, Z. Shi, Y. Guo, and J. Ye, “Object Detection in 20 Years: A Survey,” May 2019, [Online]. Available: http://arxiv.org/abs/1905.05055
Diwakar and D. Raj, “Recent Object Detection Techniques: A Survey,” International Journal of Image, Graphics and Signal Processing, vol. 14, no. 2, pp. 47–60, Apr. 2022, doi: 10.5815/ijigsp.2022.02.05.
S. Parvathi and S. Tamil Selvi, “Detection of maturity stages of coconuts in complex background using Faster R-CNN model,” Biosyst Eng, vol. 202, pp. 119–132, Feb. 2021, doi: 10.1016/j.biosystemseng.2020.12.002.
M. Sarosa and N. Muna, “IMPLEMENTASI ALGORITMA YOU ONLY LOOK ONCE (YOLO) UNTUK DETEKSI KORBAN BENCANA ALAM,” vol. 8, no. 4, 2021, doi: 10.25126/jtiik.202184407.
A. N. Sugandi and B. Hartono, “Implementasi Pengolahan Citra pada Quadcopter untuk Deteksi Manusia Menggunakan Algoritma YOLO,” 2022.
Khairunnas, E. M. Yuniarno, and A. Zaini, “Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot”.
M. Harahap, E. Sartana Agustia, M. Marsyal Lubis, A. Anggara, U. Prima Indonesia, and J. Sekip Simpang Sekambing, “Deteksi Objek Manusia Pada Image Dengan Metode Thinning Berdasarkan Local Maxima,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 7, no. 3, 2020, [Online]. Available: http://jurnal.mdp.ac.id
S. Kumari and D. Agrawal, “A Review on Video Based Vehicle Detection and Tracking using Image Processing,” 2022. [Online]. Available: www.ijrpr.com
M. Maity, S. Banerjee, and S. Sinha Chaudhuri, “Faster R-CNN and YOLO based Vehicle detection: A Survey,” in Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, Apr. 2021, pp. 1442–1447. doi: 10.1109/ICCMC51019.2021.9418274.
S. Megawan and W. S. Lestari, “Deteksi Spoofing Wajah Menggunakan Faster R-CNN dengan Arsitektur Resnet50 pada Video (Face Spoofing Detection Using Faster R-CNN with Resnet50 Architecture on Video),” 2020. [Online]. Available: https://www.idiap.ch/dataset/replayattack.
S. Singh, U. Ahuja, M. Kumar, K. Kumar, and M. Sachdeva, “Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment,” Multimed Tools Appl, vol. 80, no. 13, pp. 19753–19768, May 2021, doi: 10.1007/s11042-021-10711-8.
H. Zhu, Q. Yayun, H. Shi, N. Li, and H. Zhou, Human Detection Under UAV: an Inproved Faster R-CNN Approach.
X. Renjun, Y. Junliang, W. Yi, and S. Mengcheng, “Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example,” Geofluids, vol. 2022, 2022, doi: 10.1155/2022/7812410.
A. Khan et al., “Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization,” Computers, Materials and Continua, vol. 70, no. 2, pp. 2113–2130, 2022, doi: 10.32604/cmc.2022.018270.
Y. Wang, Z. Li, X. Yang, N. Luo, Y. Zhao, and G. Zhou, “Insulator Defect Recognition Based on Faster R-CNN,” in Proceedings of the 2020 International Conference on Computer, Information and Telecommunication Systems, CITS 2020, Oct. 2020. doi: 10.1109/CITS49457.2020.9232614.