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All Journal Sains Tanah JITRO (Jurnal Ilmiah dan Teknologi Peternakan Tropis) JOIV : International Journal on Informatics Visualization Jurnal Disastri: Pendidikan Bahasa dan Sastra Indonesia Jurnal Ekonomika dan Bisnis Islam Jurnal Pengabdian Masyarakat Bhinneka AGRIC Jurnal Kajian Islam dan Sosial Keagamaan
Wahyu Adi Nugroho
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Public Health Social Sciences Veterinary Other

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Journal : JOIV : International Journal on Informatics Visualization

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Deep Learning-based Models with YOLOv7 and Convolutional Neural Networks for Vehicle Detection and Recognition Nugroho, Wahyu Adi; Supriyanto, Catur; Safar, Noor Zuraidin Mohd
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3584

Abstract

The application of artificial intelligence (AI) technology has become prevalent across various sectors, including transportation and smart city. A key implementation of AI in this domain is traffic monitoring, often relying on license plate recognition to identify vehicles. However, this approach faces limitations when plates are obscured. To address this issue, this research explores a broader approach by recognizing general vehicle attributes, ensuring more accurate identification and comprehensive traffic statistics. The proposed solution integrates the You Only Look Once (YOLO) object detection algorithm and convolutional neural networks (CNN) pretrained models for vehicle attributes recognition. This study utilizes multiple datasets, including Roboflow Vehicle, Stanford Cars, VehicleID, and VCoR, to ensure comprehensive model evaluation. Experimental results indicate that YOLOv7 achieved a mean average precision (mAP) score of 86.1% for vehicle detection, with an average precision (AP) score of 91.5% for the car class. For vehicle make and model recognition, the lightweight EfficientNetV2S model demonstrated the highest accuracy score, achieving 89.8% and 99.2% on the Stanford Cars and VehicleID dataset, respectively. For vehicle color recognition, DenseNet201 models achieved the highest accuracy score of 87% on the VCoR dataset. These findings underscore the effectiveness of integrating YOLOv7 and CNN models for robust vehicle detection and recognition. This research provides a practical solution to the limitations of traditional license plate recognition methods, contributing to the development of more accurate and efficient traffic monitoring systems. Future studies may further optimize the framework for real-time applications and diverse traffic scenarios.
Co-Authors Ariyandi, Eka Putra Asmin Asmin, Asmin Bambang Haryanto Catur Supriyanto Elvira Rosiana Indah Fathnur, Fathnur Harmini, Harmini Imran Imran La Baco S Laode Sabaruddin Moch. Khoirul Anwar Muh Zamroni Muhammad Nur Musyadik Musyadik, Musyadik Ngatmini Nimas Ifthinatun Nabila Noni Sherlya Yasmin Ramadhani Nur Rahmawati Wahid Patriadi, Andi Prayogo, Bela Puspito, Sigit Rumihin, Ony Frengky Rusdi Rusdi Safar, Noor Zuraidin Mohd Siti Ulfiyani Solly Aryza Sudjono Utomo Theresia Danik Kristanti Wahid Wahid, Wahid Wardi Wardi Widyatmani Sih Dewi