DETEKSI PELANGGARAN KENDARAAN PADA ZEBRA CROSS MENGGUNAKAN SUPERVISION DAN PENENTUAN TINGKAT PELANGGARAN MENGGUNAKAN ALGORITMA TWO DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK PADA JALAN RAYA KOTA PALEMBANG

SARAGIH, MUHAMMAD FACHRI and Oklilas, Ahmad Fali (2023) DETEKSI PELANGGARAN KENDARAAN PADA ZEBRA CROSS MENGGUNAKAN SUPERVISION DAN PENENTUAN TINGKAT PELANGGARAN MENGGUNAKAN ALGORITMA TWO DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK PADA JALAN RAYA KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

In this study, the You Only Look Once Version 8 (YOLOv8) algorithm was used to detect and use the Supervision library to calculate the number of vehicles violating zebra crossings. The Two Dimensional Convolutional Neural Network (2DCNN) method was used to determine the level of violations with the parameters zero, low, medium, and high. This research uses a vehicle image dataset totaling 3592 images. YOLOv8 in this study produced an mAP model of 85.8% and YOLOv8 testing accuracy with Supervision of 94.39% through comparison of model detection results and manual calculation results. Furthermore, the 2DCNN method produces model training accuracy of 93% and testing accuracy of 98% and produces a low average violation rate on data consisting of 23 videos originating from three traffic intersections in the city of Palembang.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	YOLOv8, 2DCNN, Deteksi Pelanggaran, Pelanggaran Zebra Cross
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Fachri Saragih
Date Deposited:	11 Jan 2024 01:26
Last Modified:	11 Jan 2024 01:26
URI:	http://repository.unsri.ac.id/id/eprint/137827

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