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All Journal Teknik Sipil dan Arsitektur Jurnal Teknik Kimia USU Paduraksa : Jurnal Teknik Sipil Universitas Warmadewa Jurnal Buana Informatika Jurnal TIMES MLJ Jurnal Pengembangan Rekayasa dan Teknologi Jurnal AKSI (Akuntansi dan Sistem Informasi) Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Antivirus : Jurnal Ilmiah Teknik Informatika Sumatera Medical Journal bit-Tech Civilla : Jurnal Teknik Sipil Universitas Islam Lamongan JMTS: Jurnal Mitra Teknik Sipil Ilomata International Journal of Tax and Accounting Jurnal Informatika dan Sistem Informasi Jurnal Manajemen Bisnis Eka Prasetya Jurnal Teknik Sipil dan Arsitektur Jurnal Media dan Komunikasi Jurnal Profesi Insinyur Universitas Lampung STATIKA: Jurnal Teknik Sipil Kalbisocio: Jurnal Bisnis dan Komunikasi Jurnal Teknik Kimia USU eProceedings of Engineering Jurnal Manajemen Teknologi dan Sistem Informasi Simangunsong Journal of Business Administration, Management, Economic and Accounting Mount Hope Global Economic Journal Jurnal Informatika dan Bisnis BATOBO: Jurnal Pengabdian Kepada Masyarakat RESLAJ: Religion Education Social Laa Roiba Journal Perwira Journal of Economics and Business (PJEB) PROCEEDING INTERNATIONAL BUSINESS AND ECONOMICS CONFERENCE (IBEC) JPEMAS: Jurnal Pengabdian Kepada Masyarakat Welfare: Jurnal Pengabdian Masyarakat JoDENS: Journal of Digital Ecosystem for Natural Sustainability SCIENTIA JOURNAL : Jurnal Ilmiah Mahasiswa Jurnal Wana Tropika Fortunate Business Review JURNAL DATA SAINS DAN TEKNOLOGI INFORMASI (DASTIS) Jurnal Serina Abdimas REKAYASA DAN INOVASI TEKNIK SIPIL
Michael
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Journal : Jurnal Buana Informatika

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Identifikasi Kendaraan Beroda Menggunakan Algoritma YOLOv5 Michael
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

The importance of traffic density measurement in road planning has led to efforts in automation using object detection algorithms, particularly YOLO (You Only Look Once), which are replacing error-prone and time-consuming manual processes. However, challenges arise in dense traffic conditions, posing a challenge to vehicle detection accuracy. This research aims to compare the performance of vehicle detection between two YOLO approaches: multi-view layer detection and conventional detection, focusing on YOLOv5n, YOLOv5s, and YOLOv5m. The literature review encompasses Computer Vision, YOLO implementation, and related research to provide conceptual context. The research method details the steps of vehicle identification using YOLOv5, and the evaluation includes the performance of various YOLO variants and multi-view detection approaches. Thus, this study is expected to gain deeper insights into building an effective model and facilitating the selection of a suitable YOLO model for vehicle detection.
Identifikasi Kendaraan Beroda Menggunakan Algoritma YOLOv5 Michael
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The importance of traffic density measurement in road planning has led to efforts in automation using object detection algorithms, particularly YOLO (You Only Look Once), which are replacing error-prone and time-consuming manual processes. However, challenges arise in dense traffic conditions, posing a challenge to vehicle detection accuracy. This research aims to compare the performance of vehicle detection between two YOLO approaches: multi-view layer detection and conventional detection, focusing on YOLOv5n, YOLOv5s, and YOLOv5m. The literature review encompasses Computer Vision, YOLO implementation, and related research to provide conceptual context. The research method details the steps of vehicle identification using YOLOv5, and the evaluation includes the performance of various YOLO variants and multi-view detection approaches. Thus, this study is expected to gain deeper insights into building an effective model and facilitating the selection of a suitable YOLO model for vehicle detection.
Co-Authors Aditya Rachman Zarkasih Afriyadi Agustina, Shela Aleksander Purba Andreas Danny Agus W Andry , Yuliyanto Andry Yuliyanto Angeliou, Ryan Angella Simon Asriandi Ekaputra, Reza Auroka, Enjelica Beckham, Felix Butar Butar , Mangasi Butar Butar, Ribka Sari Cahyo Agung Saputra Channy Saka Charles Charlitos, Ralfpiere Christhini Dalimunthe, Nisaul Fadilah Daniel Susilo Deka Prismawan Delliana, Santi Dharma, Ernest Kusuma Dita Eka Pratiwi Sirait Donny Candra Edi Wijaya edy Estethia Ompusunggu, Sevline evyanto, winda Fauba, Andrean Feriani Astuti Tarigan Frans Dermanto Hutabarat Galih Rio Prayogi Gusrio Tendra Gutama, Detha Sekar Langit Wahyu Hanafi Dahlan, Rully Helga Meilany Humdiana Roelly Husaein, Ahmad Ig. Prasetya Dwi Wibawa Indri Rahmadhani Fitriana Ivonie Jerry Heikal Johannes Hamonangan Siregar Kambira, Pretty Falena Atmanda Kambira Khanza, Ayu Kamila Kozen, Vincent Lie, Eric Liu, Auren Lukamnul Hakim M Gilang Indra Mardika Makaba, Kharisma Austin Mareta, Arya melindawati Meta Kallista Michelle Mikhael Stefanus Filemon Simatupang Muhammad Awaluddin Muhammad Rizky Pribadi Muhammad Thoriq Al Fath Nadi, Muhammad Abi Berkah Prasetyanto, Eko Adi Princes, Elfindah Priyadi, Joko Purba, Daniel Bungaran Purwadi Putra Sanjaya Simanjuntak Putri Ayu Dwiyana Rahmandhani Fitriana, Indri Rasji, Rasji Rianti Agustini, Silvia Rio, Firstan Robinhot Gultom, Robinhot Ronaldo Budiman Ruslin Salihi Salsabila, Laira Sambodo, Bambang Samosir, Rosma Natalia Saqinah, Shandia Selly Setiawan, Preza Shafitri, Khairiyah Sherlyna Sidabutar, Rivaldi Sinta Paramita Sitepu, Andres Putranta Sri Rezeki Sudiono Sugeng Wahyudiono, Sugeng Suhartati, Tatik Susanto, Angeline Sutarno Teguh, Fiola Thang, Susanti Thiodorus Marvin Tjandra Vimala Yanthi, Valencia Wardana, Aditya Wisnu Wati Asriningsih Pranoto William Widjaja Yuwono Marta Dinata