Traffic Light Detection Computer Vision Project
Updated 10 months ago
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In this project, we will use YOLOv8 to recognise traffic light in order to demonstrate the model's efficacy in dealing with various datasets and its practical utility in real-world applications. We aim to highlight YOLOv8's wider significance in computer vision by showcasing its effectiveness in tasks requiring quick and precise object recognition. The goal of this project is to harness the capabilities of the YOLO model for Traffic light detection. The dataset used includes 67 well annotated photos of red, yellow and green traffic lights. Our goal is to train a YOLOv8 model to recognise and precisely locate these traffic lights in the provided photos. The rationale behind the selection of YOLOv8 is its advanced functionality and versatility, outperforming its predecessors in terms of accuracy and efficiency. YOLOv8's typical single-shot detection feature, combined with its high accuracy, makes it an excellent choice for scenarios requiring real-time processing
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
traffic-light-detection-ozcos_dataset,
title = { Traffic Light Detection Dataset },
type = { Open Source Dataset },
author = { sreya },
howpublished = { \url{ https://universe.roboflow.com/sreya-3iisz/traffic-light-detection-ozcos } },
url = { https://universe.roboflow.com/sreya-3iisz/traffic-light-detection-ozcos },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { jan },
note = { visited on 2024-11-21 },
}