Evaluating YOLOv5 and YOLOv8: Advancements in Human Detection
Abstract
The YOLO (You Only Look Once) method is a state-of-the-art approach in real- time object detection, known for its high-speed image processing capabilities. Recently YOLO versions have differed in performance, particularly in terms of detection accuracy and computational efficiency. The objective of this study is to assess the effectiveness and performance of YOLOv5 and YOLOv8 in real-time human detection applications using the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology also. The dataset was processed through the Roboflow platform, which facilitated both the dataset management and the labeling process. Roboflow's tools streamlined the annotation of images, ensuring consistent labeling for deep learning model training and evaluation. F1 score, recall score, and precision score are compared both YOLOv5 and YOLOv8 to evaluate the performance of these architectures. The result of the evaluations shows that the performance of the YOLOv8 is better than the YOLOv5 which, YOLOv5 achieved F1-score equal 0.5865 (58%), recall score equal 0.83 (83%), and precision score of 0.4535 (45%). Meanwhile, YOLOv8 demonstrated better performance, with F1-score of 0.7921 (79%), recall score of 0.8289 (82%), and precision score of 0.7585 (75%). Base on the evaluations, we concluded that the performance of the YOLOv8 model is greater than the YOLOv5 model for Precision, and F1-Score, while YOLOv5 has slightly better score on recall. The contribution of this study is going to implemented into Audio guidance for the blind’s prototype that have been developing in previous study.
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References
D. P. Sari and A. H. Mirza, "The detection of face recognition as employee attendance presence using the YOLO algorithm (you only look once)," Jurnal Darma Agung, vol. 30, no. 3, pp. 41-50, 2022.
H. Haeruddin, H. Herman, and P. P. Hendri, "Pengembangan aplikasi emoticon recognition dan facial recognition menggunakan algoritma local binary pattern histogram (LBPH) dan convolutional neural network (CNN)," Jurnal Teknologi Terpadu, vol. 9, no. 1, pp. 49–55, 2023, doi: 10.54914/jtt.v9i1.613.
E. Utama, F. Yapputra, and G. Gasim, "Identifikasi jenis mangga berdasarkan bentuk menggunakan fitur HOG dan jaringan syaraf tiruan," Jurnal Ilmiah Informatika Global, vol. 9, no. 1, 2018.
S. Marappan, P. Kuppuswamy, R. John, and N. Shanmugavadivu, "Human detection in still images using HOG with SVM: A novel approach," in Proc. Second Int. Conf. on Innovative Computing and Cutting-edge Technologies (ICICCT 2020), Springer International Publishing, 2021, pp. 385-397, doi: 10.1007/978-3-030-65407-8_33.
T. A. Nguyen, T. Q. Tran-Thi, D. H. Bui, and X. T. Tran, "FPGA-based human detection system using HOG-SVM algorithm," in 2023 Int. Conf. on Advanced Technologies for Communications (ATC), IEEE, 2023, pp. 72-77.
Y. He, "A novel low-resource consumption and high-speed hardware implementation of HOG feature extraction on FPGA for human detection," Integration, vol. 97, 2024, doi: 10.1016/j.vlsi.2024.102208.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Region-based convolutional networks for accurate object detection and segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 142–158, 2016, doi: 10.1109/TPAMI.2015.2437384.
Y. Wang, J. Wu, and H. Li, "Human detection based on improved Mask R-CNN," in J. Phys.: Conf. Ser., Institute of Physics Publishing, vol. 1575, no. 1, Jul. 2020, doi: 10.1088/1742-6596/1575/1/012067.
K. R. Akshatha, A. K. Karunakar, S. B. Shenoy, A. K. Pai, N. H. Nagaraj, and S. S. Rohatgi, "Human detection in aerial thermal images using faster R-CNN and SSD algorithms," Electronics (Switzerland), vol. 11, no. 7, Apr. 2022, doi: 10.3390/electronics11071151.
S. S. Sumit, J. Watada, A. Roy, and D. R. A. Rambli, "In object detection deep learning methods, YOLO shows supremum to Mask R-CNN," in J. Phys.: Conf. Ser., Institute of Physics Publishing, vol. 1529, no. 4, Jun. 2020, doi: 10.1088/1742-6596/1529/4/042086.
K. Boudjit, "Human detection based on deep learning YOLO-v2 for real-time UAV applications," J. Exp. Theor. Artif. Intell., vol. 34, no. 3, pp. 527–544, 2022, doi: 10.1080/0952813X.2021.1907793.
M. D. Klein, Z. J. Edel, C. D. Packard, J. N. Hendrickson, A. C. Levanen, and P. L. Rynes, "Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery," in Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, SPIE, vol. 13035, pp. 66-82, Jun. 2024, doi: 10.1117/12.3013911.
A. Raza, M. H. Yousaf, and S. A. Velastin, "Human fall detection using YOLO: a real-time and AI-on-the-edge perspective," in 2022 12th Int. Conf. on Pattern Recognition Systems (ICPRS), IEEE, 2022, pp. 1-6, doi: 10.1109/ICPRS54038.2022.9854070.
P. A. Cahyani, M. Mardiana, P. B. Wintoro, and M. A. Muhammad, "Sistem perhitungan kendaraan menggunakan algoritma YOLOv5 dan DeepSORT," Jurnal Teknik Informatika dan Sistem Informasi, vol. 10, no. 1, May 2024, doi: 10.28932/jutisi.v10i1.7519.
D. I. Mulyana and M. A. Rofik, "Implementasi deteksi real time klasifikasi jenis kendaraan di Indonesia menggunakan metode YOLOV5," Jurnal Pendidikan Tambusai, vol. 6, no. 3, 2022, pp. 13971-13982.
N. P. Motwani and S. S, "Human activities detection using deep learning technique- YOLOv8," ITM Web of Conferences, vol. 56, p. 03003, 2023, doi: 10.1051/itmconf/20235603003.
N. Hidayat, S. Wahyudi, and A. A. Diaz, "Pengenalan individu melalui identifikasi wajah menggunakan metode You Only Look Once (Yolov5)," UNEJ e-Proceeding, pp. 85-98, 2022.
N. Ma’muriyah, A. Yulianto, and Lili, "Design prototype of audio guidance system for blind by using raspberry pi and fuzzy logic controller," J. Phys. Conf. Ser., vol. 1230, no. 1, 2019, doi: 10.1088/1742-6596/1230/1/012024.
A. Setiyadi, E. Utami, and D. Ariatmanto, "Analisa kemampuan algoritma YOLOv8 dalam deteksi objek manusia dengan metode modifikasi arsitektur," J-SAKTI (Jurnal Sains Komputer dan Informatika), vol. 7, no. 2, pp. 891-901, 2023.
P. D. Manalu and Z. Situmorang, "Face recognition using support vector machine (SVM) and backpropagation neural network (BNN) methods to identify gender on student identity cards," in Proc. of International Conference on Information Science and Technology Innovation (ICoSTEC), vol. 3, no. 1, Feb. 2024.
Z. Ahmad, S. Yaacob, R. Ibrahim, and W. F. Wan Fakhruddin, "The review for visual analytics methodology," in HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, 2022, doi: 10.1109/HORA55278.2022.9800100.
Y. A. Suwitono and F. J. Kaunang, "Implementasi algoritma convolutional neural network (CNN) untuk klasifikasi daun dengan metode data mining SEMMA menggunakan Keras," Jurnal Komtika (Komputasi dan Informatika), vol. 6, no. 2, pp. 109–121, 2022, doi: 10.31603/komtika.v6i2.8054.
P. D. Manalu and Z. Situmorang, "Face recognition using support vector machine (SVM) and backpropagation neural network (BNN) methods to identify gender on student identity cards," in Proc. of International Conference on Information Science and Technology Innovation (ICoSTEC), vol. 3, no. 1, Feb. 2024.
D. Arifadilah and A. Pambudi, "Sunda script detection using You Only Look Once algorithm," J. Artif. Intell. Eng. Appl. (JAIEA), vol. 3, no. 2, pp. 606-613, 2024.
• P. K. Pativada, "Real-time detection and classification of plant seeds using YOLOv8 object detection model," 2024.


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