Meta-YOLOv8: meta-learning-enhanced YOLOv8 for precise traffic light color detection in ADAS
File(s)
Published version
Author(s)
Tammisetti, Vasu
Stettinger, Georg
Cuellar, Manuel Pegalajar
Molina-Solana, Miguel
Type
Journal Article
Abstract
The ability to accurately detect traffic light color is critical for the functioning of Advanced Driver Assistance Systems (ADAS), as it directly impacts a vehicle’s safety and operational efficiency. This paper introduces Meta-YOLOv8, an improvement over YOLOv8 based on meta-learning, designed explicitly for traffic light color detection focusing on color recognition. In contrast to conventional models, Meta-YOLOv8 focuses on the illuminated portion of traffic signals, enhancing accuracy and extending the detection range in challenging conditions. Furthermore, this approach reduces the computational load by filtering out irrelevant data. An innovative labeling technique has been implemented to address real-time weather-related detection issues, although other bright objects may occasionally confound it. Our model employs meta-learning principles to mitigate confusion and boost confidence in detections. Leveraging task similarity and prior knowledge enhances detection performance across diverse lighting and weather conditions. Meta-learning also reduces the necessity for extensive datasets while maintaining consistent performance and adaptability to novel categories. The optimized feature weighting for precise color differentiation, coupled with reduced latency and computational demands, enables a faster response from the driver and reduces the risk of accidents. This represents a significant advancement for resource-constrained ADAS. A comparative assessment of Meta-YOLOv8 with traditional models, including SSD, Faster R-CNN, and Detection Transformers (DETR), reveals that it outperforms these models, achieving an F1 score, accuracy of 93% and a precision rate of 97%.
Date Issued
2025-02-01
Date Acceptance
2025-01-12
Citation
Electronics, 2025, 14 (3)
ISSN
2079-9292
Publisher
MDPI AG
Journal / Book Title
Electronics
Volume
14
Issue
3
Copyright Statement
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).
License URL
Subjects
advanced driver assistance system (ADAS)
autonomous vehicle (AV)
Computer Science
Computer Science, Information Systems
convolutional neural networks (CNN)
Engineering
Engineering, Electrical & Electronic
labeling
meta-learning
meta-YOLO
optimization
Physical Sciences
Physics
Physics, Applied
Science & Technology
task similarity
Technology
YOLO
Publication Status
Published
Article Number
468
Date Publish Online
2025-01-24