A Comparison of YOLO Models for License Plate Detection
DOI:
https://doi.org/10.17010/ijcs/2021/v6/i6/167643Keywords:
Image recognition
, LPDR, YOLO Model.October 3
, 2021, Revised, November 15, Accepted, November 20, 2021. Date of Publication, December 5, 2021.Abstract
License plate detection and recognition (LPDR) has become an increasingly popular field of study under image recognition. The ability to autonomously create a bounding box around a license plate using machine learning models for image recognition is an important component of intelligent transportation systems. The YOLO model has become a popular CNN model for image detection because of its speed and accuracy. This work focuses on the use of the YOLO model for object localization as applied to license plate detection. The research compares the accuracy and speed between the YOLOv3 and YOLOv4 models along with various configuration parameters and finds that the YOLOv4 model is most accurate while the YOLOv3 with reduced image resolution is the fastest in prediction time.Downloads
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S. Pal and S. K. Behera, “Traffic sign recognition for self driving vehicles using matlab and tensorflow,†Indian J. Comput. Sci., vol. 5, no. 6, Nov.-Dec. 2020, doi: 10.17010/ijcs/2020/v5/i6/157502
O. Russakovsky et al., "ImageNet large scale visual recognition challenge." 2015. [Online]. Available: arXiv: 1409.0575 [cs.CV]
P. Dwivedi. “YOLOv5 compared to faster RCNN. Who Wins?†Towards data science.com. (accessed : May 7, 2021). https://towardsdatascience.com/yolov5compared-to-faster-rcnn-who-wins-a771cd6c9fb4. .
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. "You only look once: Unified, real-time object detection," in 2016 IEEE Conf. Comput. Vision Pattern Recognition (CVPR), 2016, pp. 779-788. [Online]. A v a i l a b l e : h t t p s : / / w w w . c v foundation.org/openaccess/content_cvpr_2016/papers/ Redmon_You_Only_Look_CVPR_2016_paper.pdf
G. Karimi. “Introduction to Yolo algorithm for object d e t e c t i o n , †S e c t i o n . i o . h t t p s : / / w w w . s e c t i o n . i o / e n g i n e e r i n g education/introduction-to-yolo-algorithm-for-objectdetection/ (accessed May 7, 2021).
“Car License Plate Detection, Version 1â€, Kaggle, Sep. 2020. https://www.kaggle.com/andrewmvd/carplatedetection/activity (accessed May 10, 2021).
] D. RadeÄić. “How to detect license plates with Python and YOLO.†Towards Data Science.com. https://towardsdatascience.com/how-to-detect-licenseplateswith-python-and-yolo-8842aa6d25f7 (accessed May 6, 2021).
P. Canuma. “Image pre-processing.†Medium.com. https://prince-canuma.medium.com/image-preprocessingc1aec0be3edf (accessed May 9, 2021).
J. Solawetz. “YOLOv4 - Ten tactics to build a better model.†robloflow. https://blog.roboflow.com/yolov4tactics/ (accessed May 12, 2021).
A. Rosebrock. “Intersection over Union (IOU) for O b j e c t D e t e c t i o n . †P y I m a g e S e a r c h . c o m , https://www.pyimagesearch.com/2016/11/07/intersectio n-over-union-iou-for-object-detection/ (accessed May 18, 2021).
R. Rajkumar, A. Kaushal, and A. Saha, “Accelerating machine learning research using transfer learning,†Indian J. Comput. Sci., vol. 3, no. 2, Mar.-Apr. 2018, doi: 10.17010/ijcs/2018/v3/i2/123212
T. C. Arlen. “Understanding the MAP evaluation metric for object detection.†Medium.com. https://medium.com/@timothycarlen/understandingthemap-evaluation-metric-for-object-detectiona07fe6962cf3 (accessed March 7, 2021).
J. Hui, “Map (Mean Average Precision) for Object Detection.†Medium.com. [Online]. Available: https://jonathan-hui.medium.com/map-mean-averageprecisionfor-object-detection-45c121a31173 (accessed May 14, 2021).
J. Nelson, “When to use contrast as a preprocessing step,†roboflow.com. https://blog.roboflow.com/whentouse-contrast-as-a-preprocessing-step/ (accessed May 19, 2021).
