RAFI, AHMAD NANDA YUMA (2024) LINE CROSSING DETECTOR SYSTEM FOR REAL-TIME OVER-TAKING VEHICLE DETECTION. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This study introduces a novel method for detecting overtaking vehicles by integrating Virtual Line Detection with the YOLOv8n algorithm. The objective is to enhance road safety by accurately identifying and tracking vehicles as they overtake, which is crucial for preventing. The research demonstrates the effectiveness of this approach, achieving a detection accuracy rate of 80.95% using line crossing detection techniques. This high level of accuracy underscores the potential of the system to reliably identify overtaking maneuvers in traffic conditions. Furthermore, this innovative method holds promising implications for enhancing safety riding by providing real-time alerts to drivers and preventing infrastructure loss resulting from traffic incidents. The findings suggest that integrating advanced detection algorithms like YOLOv8n with virtual line detection can be a viable solution for modern traffic safety challenges. Keywords: YOLOv8n, Vehicle Detection, Line Crossing Detector, CNN Penelitian ini memperkenalkan metode baru untuk mendeteksi kendaraan yang sedang melampaui kendaraan lain dengan mengintegrasikan Line Crossing Detector dengan algoritma YOLOv8n. Tujuannya adalah untuk meningkatkan keselamatan jalan dengan mengidentifikasi dan melacak kendaraan yang mendahului secara akurat, yang sangat penting untuk mencegah kecelakaan. Penelitian ini menunjukkan efektivitas dari metode deteksi yang diusulkan mencapai tingkat akurasi deteksi sebesar 80,95% menggunakan teknik deteksi lintasan garis. Tingkat akurasi yang tinggi ini menunjukkan potensi sistem untuk mengidentifikasi manuver kendaaraan saat mendahului dengan. Selain itu, metode inovatif ini memiliki implikasi menjanjikan untuk meningkatkan keselamatan berkendara dengan memberikan peringatan real-time kepada pengemudi dan mencegah kerugian infrastruktur akibat insiden lalu lintas. Hasil penelitian ini menunjukkan bahwa mengintegrasikan algoritma deteksi canggih seperti YOLOv8n dengan deteksi garis virtual dapat menjadi solusi yang layak untuk tantangan keselamatan lalu lintas modern. Kata Kunci: YOLOv8n, Vehicle Detection, Line Crossing Detector, CNN.
| Item Type: | Thesis (S1) |
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| Call Number CD: | FIK/INFO. 24 110 |
| Call Number: | SIK/15/24/076 |
| NIM/NIDN Creators: | 41520010158 |
| Uncontrolled Keywords: | YOLOv8n, Vehicle Detection, Line Crossing Detector, CNN. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 700 Arts/Seni, Seni Rupa, Kesenian > 710 Civic and Lanscape Art/Seni Perkotaan dan Pertamanan > 711 Area Planning/Perencanaan Wilayah Perkotaan dan Pertamanan > 711.7 Plans for Transportation Facilities/Perencanaan untuk Fasilitas Transportasi > 711.73 Motor Vehicle Transportation Facilities/Fasilitas Kendaraan Motor |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | khalimah |
| Date Deposited: | 18 Jul 2024 01:59 |
| Last Modified: | 18 Jul 2024 01:59 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/89596 |
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