Version 1
: Received: 26 September 2024 / Approved: 26 September 2024 / Online: 26 September 2024 (13:27:42 CEST)
How to cite:
Nguyen, L. A.; Son, Y.; Tran, M. D. Empirical Evaluation and Analysis of YOLO Models in Smart Transportation. Preprints2024, 2024092125. https://doi.org/10.20944/preprints202409.2125.v1
Nguyen, L. A.; Son, Y.; Tran, M. D. Empirical Evaluation and Analysis of YOLO Models in Smart Transportation. Preprints 2024, 2024092125. https://doi.org/10.20944/preprints202409.2125.v1
Nguyen, L. A.; Son, Y.; Tran, M. D. Empirical Evaluation and Analysis of YOLO Models in Smart Transportation. Preprints2024, 2024092125. https://doi.org/10.20944/preprints202409.2125.v1
APA Style
Nguyen, L. A., Son, Y., & Tran, M. D. (2024). Empirical Evaluation and Analysis of YOLO Models in Smart Transportation. Preprints. https://doi.org/10.20944/preprints202409.2125.v1
Chicago/Turabian Style
Nguyen, L. A., Yongseok Son and Manh Dat Tran. 2024 "Empirical Evaluation and Analysis of YOLO Models in Smart Transportation" Preprints. https://doi.org/10.20944/preprints202409.2125.v1
Abstract
You Only Look One (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models,
choosing selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (e.g., COCO, PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications.
Third, multiple intersection-over-union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. Thus, YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss.
Keywords
Convolution neural network (CNN); real-time object detection; road transportation; smart transportation; You Only Look Once (YOLO)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
