Automated Traffic Sign Detection and Recognition using Deep Learning

Project Domain / Category

Deep Learning / Computer Vision

Abstract / Introduction

The aim of this project is to develop an automated system that utilizes deep learning and computer vision to detect and recognize traffic signs in real-time. Accurate identification of traffic signs is critical for enhancing road safety and supporting the development of autonomous driving technologies. By leveraging a collection of traffic sign images annotated with various sign types (e.g., stop signs, speed limits, yield signs, etc.), the system will train a Convolutional Neural Network (CNN) to effectively detect and classify traffic signs under diverse environmental conditions.

This system is designed to assist autonomous vehicle systems and advanced driver-assistance systems (ADAS) by providing reliable traffic sign recognition, ultimately contributing to safer navigation and better compliance with road regulations.

Datasets for reference:

https://www.kaggle.com/datasets/pkdarabi/cardetection https://www.kaggle.com/datasets/ahemateja19bec1025/traffic-sign-dataset-classification https://universe.roboflow.com/usmanchaudhry622-gmail-com/traffic-and-road-signs

Functional Requirements:

Admin (Student) will perform all these tasks.

1.      Data Collection:

o    Compile datasets containing images of various traffic signs under different weather, lighting, and angle conditions.

o    Utilize publicly available datasets such as GTSRB and LISA, or curate a custom dataset if needed.

2.      Image Preprocessing:

o    Implement preprocessing routines including cropping, resizing, normalization, and augmentation to improve image quality and dataset robustness.

o    Optimize the images for effective deep learning model training.

3.      Traffic Sign Detection and Recognition:

o    Develop and train a CNN-based model to detect traffic signs and classify them into predefined categories.

o    Explore both object detection frameworks (such as YOLO or SSD) for sign localization and classification networks for recognition.

o    Fine-tune the model to achieve high accuracy and real-time performance.