Comparative Analysis of YOLOv8 and YOLOv10 in Vehicle Detection: Performance Metrics and Model Efficacy

Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection acr...

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Bibliographic Details
Published in:Vehicles 2024-09, Vol.6 (3), p.1364-1382
Main Authors: Athulya Sundaresan Geetha, Mujadded Al Rabbani Alif, Hussain, Muhammad, Allen, Paul
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automobiles
autonomous driving
Bicycles
Boxes
Buses (vehicles)
Computer vision
deep learning
Effectiveness
In vehicle
Intelligent transportation systems
Literature reviews
Localization
Machine learning
Monitoring systems
Motorcycles
Neural networks
object classification
performance comparison
Performance evaluation
Performance measurement
Roads & highways
Task complexity
Transportation models
Trucks
vehicle detection
Vehicles
YOLO architectures
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Summary:Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection across multiple classes such as bicycles, buses, cars, motorcycles, and trucks. Using a range of performance metrics, including precision, recall, F1 score, and detailed confusion matrices, we evaluate the performance characteristics of each model.The findings reveal that YOLOv10 generally outperformed YOLOv8, particularly in detecting smaller and more complex vehicles like bicycles and trucks, which can be attributed to its architectural enhancements. Conversely, YOLOv8 showed a slight advantage in car detection, underscoring subtle differences in feature processing between the models. The performance for detecting buses and motorcycles was comparable, indicating robust features in both YOLO versions. This research contributes to the field by delineating the strengths and limitations of these models and providing insights into their practical applications in real-world scenarios. It enhances understanding of how different YOLO architectures can be optimized for specific vehicle detection tasks, thus supporting the development of more efficient and precise detection systems.
ISSN:2624-8921
DOI:10.3390/vehicles6030065