Vehicle Identification and Classification Using YOLO Algorithm

Chau Ly Thi Huyen; Chi Pham Quyet; Quyen Thi Nguyen

doi:10.31436/iiumej.v27i1.3694

Authors

Chau Ly Thi Huyen Van Lang University https://orcid.org/0000-0002-7868-6888
Chi Pham Quyet Van Lang University
Quyen Thi Nguyen Van Lang University https://orcid.org/0000-0001-5317-6550

DOI:

https://doi.org/10.31436/iiumej.v27i1.3694

Keywords:

YOLOv8, Vehicle identification, Vehicle classification, COCO datasets

Abstract

Vehicle identification and classification are among the challenging activities for the management and control of a large number of different vehicles moving in the inner city. Among many identification and classification systems, the YOLO algorithm stands out for its ability to analyze at high speed and with high accuracy. The algorithm is continually evolving, with notable versions including YOLOv8. This research presents a method for identifying and classifying vehicles using the YOLOv8 algorithm. The assessment of the proposed method's effectiveness was conducted using two COCO datasets (328,000 images) and a real-world dataset from Ho Chi Minh City (HCMC) with more than 1,000 images. The findings indicate that the proposed method can be applied to identify and classify vehicles with an accuracy of 93%-98%. Comparative results with prior studies also demonstrate the superiority of the YOLOv8 algorithm.

ABSTRAK: Pengecaman dan pengelasan kenderaan adalah salah satu aktiviti mencabar dalam pengurusan dan kawalan sejumlah besar kenderaan berbeza yang bergerak di bandar. Di antara kebanyakan sistem pengecaman dan pengelasan, algoritma YOLO menonjol kerana keupayaannya menganalisa pada kelajuan berketepatan tinggi. Algoritma ini terus dibangunkan dengan penambahbaikan yang banyak dan versi yang ketara ialah YOLOv8. Penyelidikan ini membentangkan kaedah mengenal pasti dan mengelaskan kenderaan menggunakan algoritma YOLOv8. Penemuan penilaian keberkesanan kaedah yang dicadangkan telah dijalankan menggunakan dua set data COCO dengan 328,000 imej dan set data sebenar di Ho Chi Minh City (HCMC) lebih daripada 1,000 imej. Dapatan kajian menunjukkan kaedah ini diaplikasikan dalam mengenal pasti dan pengelasan kenderaan berketepatan 93% hingga 98%. Hasil perbandingan dengan kajian lepas juga menunjukkan keunggulan algoritma YOLOv8.

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