Implementasi Metode Yolo pada Deteksi Objek Manusia
DOI:
https://doi.org/10.46880/jmika.Vol8No2.pp234-240Keywords:
Humans, Convolution, Object Detection, Yolo, Deep LearningAbstract
Until now, the problem of theft of motorbikes and livestock in North Sumatra is still quite high. Locations for motorbike theft can occur in many places such as schools, homes, parking lots, offices and so on, while for livestock it can occur on pastures and in pens during the day or night and the perpetrators are men. To make this theft a success, various modes are used in varying human positions, from sitting, squatting to standing. To help overcome this, several object detection methods have been developed such as Background Subtraction, Template Matching, Histogram Oriented Gradient (HOG), Deformable Part-based Model (DPM) and Viola Jones (VJ). Of the many methods that have been used, there are still shortcomings, namely in terms of time, accuracy and various human positions. For this reason, research was carried out with the aim of improving the time and level of accuracy in detecting human objects using the YOLO method. The research stages carried out in this research include literature study, collecting data, determining training and test data, creating programs, training, and testing. From the trials carried out, it is known that YOLO can detect humans in various positions with a mAP value of 0.99 and an average detection time of 810.01 ms.
References
Anin, Alex, K., Ilya, S., & E Geoffrey, H. (2012). Imagenet classification with deep convolutional neural networks. NIPS Conference, 1097–1105.
Delia, R. (2009). Analisis Determinan Penyebab Timbulnya Fear of Crime Pada Kasus Pencurian Di Kalangan Ibu Rumah Tangga. Indonesian Journal of Criminology, 5(1), 1–5.
Felzenszwalb, P., B. Girshick, R., McAllester, D., & Ramanan, D. (2009). Object Detection with Discriminatively Trained Part Based Models. Computer, 47(2), 1–19.
Hariyanto, E., Iqbal, M., Siahaan, A. P. U., Saragih, K. S., & Batubara, S. (2019). Comparative study of tiger identification using template matching approach based on edge patterns. Journal of Physics: Conference Series, 1196(1). https://doi.org/10.1088/1742-6596/1196/1/012025
Herdianto. (2019). Perbandingan Metode Template Matching dengan Background Subtraction untuk Mendeteksi Objek Manusia. Core IT, 7(2), 28–33.
Herdianto, H., & Mursyidah, M. (2022). Deteksi Wajah Manusia Pada Image Sequence Menggunakan Background Subtraction Dan Haar Cascade Classifier. 7(1).
Herdianto, H., & Nasution, D. (2022). Klasifikasi Objek Menggunakan Metode Convolutional Neural Network (CNN). SNASTIKOM, 1–8.
Herdianto, H., & Nasution, D. (2023). Implementasi Metode Cnn Untuk Klasifikasi Objek. METHOMIKA Jurnal Manajemen Informatika Dan Komputerisasi Akuntansi, 7(1), 54–60. https://doi.org/10.46880/jmika.vol7no1.pp54-60
Lim, J. S., Astrid, M., Yoon, H. J., & Lee, S. I. (2021). Small Object Detection using Context and Attention. 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021, 181–186. https://doi.org/10.1109/ICAIIC51459.2021.9415217
Nababan, E. B., Iqbal, M., & Rahmat, R. F. (2017). Breast cancer identification on digital mammogram using Evolving Connectionist Systems. 2016 International Conference on Informatics and Computing, ICIC 2016, Icic, 132–136. https://doi.org/10.1109/IAC.2016.7905703
Navneet, D., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1–8. https://doi.org/10.1007/978-3-642-33530-3_8
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 779–788. https://doi.org/10.1109/CVPR.2016.91
Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 6517–6525. https://doi.org/10.1109/CVPR.2017.690
Sikumbang, S., & Suryadi, K. (2015). Human Detection Menggunakan Metode Histogram Of Oriented Gradients (Hog) Berbasis Open CV. Jurnal Pendidikan Teknik Elektro, 4(2), 1–6.
Viola, P., & Jones, M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. Conference On Computer Vision And Pattern Recognition, 1–9.
Viola, P., & Jones, M. (2004). Robust Real-Time Face Detection Intro to Face Detection. International Journal of Computer Vision, 57(2), 137–154.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Herdianto Herdianto, Hafni Hafni, Darmeli Nasution, Syahrul Ramadhan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
