Sistem Pencatatan Pelanggaran Kecepatan Kendaraan Berbasis Multikamera Menggunakan Deep Learning

Saputra, Muhammad Irsyad Rafi (2025) Sistem Pencatatan Pelanggaran Kecepatan Kendaraan Berbasis Multikamera Menggunakan Deep Learning. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pelanggaran kecepatan kendaraan menjadi salah satu penyebab utama kecelakaan di jalan raya. Sistem pemantauan manual seringkali tidak efektif karena keterbatasan sumber daya dan kondisi lalu lintas yang padat. Penelitian ini bertujuan merancang sistem pencatatan pelanggaran kecepatan kendaraan yang lebih unggul dengan mengintegrasikan teknologi multikamera dan deep learning. Sistem ini dirancang untuk mendeteksi, melacak, dan menghitung kecepatan kendaraan secara akurat, serta mengatasi keterbatasan cakupan area dan oklusi pada sistem konvensional. Dengan memanfaatkan algoritma deep learning untuk deteksi objek dan pemrosesan data paralel dari beberapa kamera, sistem ini diharapkan dapat meningkatkan akurasi deteksi. Hasil yang diharapkan menunjukkan bahwa pendekatan multikamera dan deep learning mampu mendeteksi pelanggaran kecepatan dengan akurasi tinggi. Sistem ini berpotensi membantu pihak berwenang dalam penegakan hukum yang lebih efektif, meningkatkan keselamatan jalan, dan menjadi dasar pengembangan teknologi pemantauan lalu lintas di masa depan.
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Vehicle speed violations are one of the main causes of road accidents. Manual monitoring systems are often ineffective due to limited resources and heavy traffic conditions. This research aims to design a superior vehicle speed violation recording system by integrating multicamera technology and deep learning. The system is designed to accurately detect, track and calculate vehicle speed, while overcoming the area coverage and occlusion limitations of conventional systems. By utilizing deep learning algorithms for object detection and parallel data processing from multiple cameras, the system is expected to improve detection accuracy. The expected results show that the multicamera and deep learning approach is able to detect speed violations with high accuracy. The system has the potential to assist authorities in more effective law enforcement, improve road safety, and form the basis for the development of future traffic monitoring technologies.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Lalu Lintas, Kendaraan, Kecepatan, Multikamera, Deep Learning, Traffic, Vehicle, Speed, Multicamera
Subjects:	T Technology > T Technology (General) > T11 Technical writing. Scientific Writing T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T57.5 Data Processing T Technology > TA Engineering (General). Civil engineering (General) > TA1573 Detectors. Sensors T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques. Image analysis--Data processing. T Technology > TE Highway engineering. Roads and pavements > TE228.3 Intelligent transportation systems.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering > 90243-(S1) Undergraduate Thesis
Depositing User:	Muhammad Irsyad Rafi Saputra
Date Deposited:	17 Jun 2025 03:00
Last Modified:	17 Jun 2025 03:00
URI:	http://repository.its.ac.id/id/eprint/119164

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