@article{ART003109582},
author={Joon-Yong Kim},
title={Comparison analysis of YOLOv10 and existing object detection model performance},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={8},
pages={85-92}
TY - JOUR
AU - Joon-Yong Kim
TI - Comparison analysis of YOLOv10 and existing object detection model performance
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 8
PB - The Korean Society Of Computer And Information
SP - 85
EP - 92
SN - 1598-849X
AB - In this paper presents a comparative analysis of the performance between the latest object detection model, YOLOv10, and its previous versions. YOLOv10 introduces NMS-Free training, an enhanced model architecture, and an efficiency-centric design, resulting in outstanding performance. Experimental results using the COCO dataset demonstrate that YOLOv10-N maintains high accuracy of 39.5% and low latency of 1.84ms, despite having only 2.3M parameters and 6.7G floating-point operations (FLOPs). The key performance metrics used include the number of model parameters, FLOPs, average precision (AP), and latency. The analysis confirms the effectiveness of YOLOv10 as a real-time object detection model across various applications. Future research directions include testing on diverse datasets, further model optimization, and expanding application scenarios. These efforts aim to further enhance YOLOv10's versatility and efficiency.
KW - YOLOv10;Object Detection;NMS-Free Training;Model Efficiency;Real-Time Processing
DO -
UR -
ER -
Joon-Yong Kim. (2024). Comparison analysis of YOLOv10 and existing object detection model performance. Journal of The Korea Society of Computer and Information, 29(8), 85-92.
Joon-Yong Kim. 2024, "Comparison analysis of YOLOv10 and existing object detection model performance", Journal of The Korea Society of Computer and Information, vol.29, no.8 pp.85-92.
Joon-Yong Kim "Comparison analysis of YOLOv10 and existing object detection model performance" Journal of The Korea Society of Computer and Information 29.8 pp.85-92 (2024) : 85.
Joon-Yong Kim. Comparison analysis of YOLOv10 and existing object detection model performance. 2024; 29(8), 85-92.
Joon-Yong Kim. "Comparison analysis of YOLOv10 and existing object detection model performance" Journal of The Korea Society of Computer and Information 29, no.8 (2024) : 85-92.
Joon-Yong Kim. Comparison analysis of YOLOv10 and existing object detection model performance. Journal of The Korea Society of Computer and Information, 29(8), 85-92.
Joon-Yong Kim. Comparison analysis of YOLOv10 and existing object detection model performance. Journal of The Korea Society of Computer and Information. 2024; 29(8) 85-92.
Joon-Yong Kim. Comparison analysis of YOLOv10 and existing object detection model performance. 2024; 29(8), 85-92.
Joon-Yong Kim. "Comparison analysis of YOLOv10 and existing object detection model performance" Journal of The Korea Society of Computer and Information 29, no.8 (2024) : 85-92.
