RIYANTO, MARK SENO (2023) Penerapan Deep Learning dan Algoritma YOLO V3 untuk Deteksi Kendaraan secara Otomatis. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Deteksi kendaraan secara otomatis merupakan topik penelitian yang penting dalam bidang visi komputer.(Yılmaz et al., 2018) Penelitian ini bertujuan untuk mengkaji penerapan teknik Deep Learning dan algoritma YOLO (You Only Look Once) dalam deteksi kendaraan secara otomatis. Deep Learning menggunakan jaringan saraf tiruan yang dapat belajar fitur-fitur kendaraan dari data gambar yang besar dan kompleks. Algoritma YOLO, di sisi lain, adalah metode efisien yang membagi gambar menjadi grid dan menghasilkan kotak pembatas serta probabilitas kelas objek yang ada di dalamnya.(Redmon & Farhadi, 2018) Dalam penelitian ini, dilakukan implementasi arsitektur jaringan saraf konvolusi yang dalam menggunakan Deep Learning, serta penerapan algoritma YOLO untuk mendeteksi kendaraan dalam gambar. Sebuah dataset yang luas dan beragam digunakan untuk melatih jaringan saraf, terdiri dari gambar kendaraan dalam berbagai kondisi dan lingkungan. Hasil eksperimen menunjukkan bahwa penerapan Deep Learning dan algoritma YOLO dapat menghasilkan deteksi kendaraan yang akurat secara otomatis.(Bochkovskiy et al., 2020) Penelitian ini memberikan kontribusi dalam pengembangan sistem deteksi kendaraan otomatis yang dapat meningkatkan keamanan lalu lintas, manajemen jalan, dan mendukung perkembangan teknologi kendaraan otonom. Selain itu, penelitian ini juga membahas potensi pengembangan dan penerapan lebih lanjut dari sistem deteksi kendaraan ini, termasuk integrasi dengan teknologi kendaraan otonom dan sistem transportasi cerdas. Kata kunci : Deep Learning, Algoritma YOLO, deteksi kenda raan otomatis, visi komputer, jaringan saraf konvolusi, dataset, teknologi kendaraan otonom. Automatic vehicle detection is a crucial research topic in the field of computer vision.(Yılmaz et al., 2018) This study aims to investigate the implementation of Deep Learning techniques and the YOLO (You Only Look Once) algorithm for automatic vehicle detection. Deep Learning utilizes artificial neural networks to learn vehicle features from large and complex image data. On the other hand, the YOLO algorithm is an efficient method that divides images into grids and generates bounding boxes along with class probabilities of objects.(Redmon & Farhadi, 2018) In this research, we implemented a deep convolutional neural network architecture using Deep Learning and applied the YOLO algorithm for vehicle detection in images. A diverse and extensive dataset consisting of vehicle images in various conditions and environments was used to train the neural network. The experimental results demonstrate that the implementation of Deep Learning and the YOLO algorithm can achieve accurate automatic vehicle detection.(Bochkovskiy et al., 2020) This research contributes to the development of automated vehicle detection systems, which can enhance traffic safety, road management, and support the advancement of autonomous vehicle technology. Additionally, the study discusses the potential for further development and implementation of this vehicle detection system, including integration with autonomous vehicle technology and intelligent transportation systems. Keywords: Deep Learning, YOLO algorithm, automatic vehicle detection, computer vision, convolutional neural networks, dataset, autonomous vehicle technology.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO 23 044 |
| NIM/NIDN Creators: | 41519310020 |
| Uncontrolled Keywords: | Deep Learning, Algoritma YOLO, deteksi kenda raan otomatis, visi komputer, jaringan saraf konvolusi, dataset, teknologi kendaraan otonom. |
| 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 |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 29 Sep 2023 03:58 |
| Last Modified: | 29 Sep 2023 03:58 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/81626 |
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