Application of You Only Look Once and DeepSORT for Vehicle Number Plate Detection
DOI:
https://doi.org/10.61769/telematika.v20i2.676Keywords:
object detection, license plate detection, convolutional neural network, YOLOv8, DeepSORTAbstract
Number plate detection is essential in traffic monitoring, law enforcement, and intelligent transport systems. However, existing methods still have difficulty accurately tracking vehicles in heavy traffic conditions. This study addresses this by combining the YOLOv8 detection model and DeepSORT tracking. Using 453 images from Kaggle, this study analyses the effect of batch size variation and an epoch on model performance. The best model achieved 95.5% precision, 95.1% recall, 98.7% mAP50, and 64.5% mAP95. The integration of YOLOv8 and DeepSORT can improve tracking consistency, reduce ID switching errors, and increase the reliability of the automatic number plate recognition system.
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Copyright (c) 2025 Firhat Hidayat, Natanael Billy, Nicholas Russel Permana, Matthew Evans Hariady
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