An Improved Traffic Light Colour Detection and Recognition System for Autonomous Vehicles

Simple item page
dc.contributor.authorTemilade Temitope FASINA
dc.date.accessioned2025-06-24T15:46:03Z
dc.date.available2025-06-24T15:46:03Z
dc.date.issued2023-12
dc.description.abstractThis study introduces significant advancements in traffic light detection and recognition using an improved YOLOv4 algorithm. Two key optimization techniques, shallow feature enhancement and bounding box uncertainty prediction, were incorporated to address the limitations of the original YOLOv4 algorithm. The results demonstrate substantial improvements in accuracy for traffic light detection and recognition. In the experiments conducted with the LISA traffic light dataset, the AUC (Area Under the Curve) increased to 97.03% and 95.31% for the two datasets of LISA and LaRa, respectively, in traffic light detection. Additionally, the map (mean Average Precision) improved to 81.34% and 78.88% for recognition trials. Despite a slight increase in detection time, the system remained capable of real-time traffic light detection. The use of bounding box uncertainty prediction further enhanced the YOLOv4 algorithm, resulting in AUC values of 96.84% and 94.73%, as well as mAP values of 79.93% and 78.23% for the LISA and LaRa datasets in traffic light detection. Importantly, this enhancement reduced detection times to 27.59 and 33.45 milliseconds, respectively. To further improve traffic light detection and recognition systems, it is recommended that the collection of diverse and extensive datasets, accurate annotation of data, data augmentation, semantic segmentation, real-time object tracking, the utilization of deep learning models, transfer learning, proper calibration, multimodal sensor fusion, redundancy, real-time processing, machine learning anomaly detection, continuous testing, and regulatory compliance are done. Keywords: Machine Learning, Traffic Light Recognition, Deep Learning, Autonomous Vehicle Word Count: 225
dc.identifier.citationKate Turabia
dc.identifier.otherM.Sc
dc.identifier.urihttps://repository.lcu.edu.ng/handle/123456789/1005
dc.language.isoen
dc.publisherLead City University, Ibadan
dc.relation.ispartofseriesM.Sc
dc.subjectMachine Learning
dc.subjectTraffic Light Recognition
dc.subjectDeep Learning
dc.subjectAutonomous Vehicle
dc.titleAn Improved Traffic Light Colour Detection and Recognition System for Autonomous Vehicles
dc.typeThesis

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Temilade Temitope Fasina full thesis.pdf
Size:
13.19 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download

Collections

Department of Computer Science;