Indian Traffic Sign Detection and Recognition System

This project aims to develop a real-time Indian traffic sign detection and recognition system using state-of-the-art deep learning models, specifically YOLOv8 and RCNN. The system is designed to accurately detect and classify traffic signs in dynamic scenarios, providing precise localization and scoring mechanisms for adaptability.

Overview

The system is implemented using the following components:

Dataset:

• The dataset used for training and testing the models is sourced from Kaggle, consisting of Indian traffic sign images.

• The images are preprocessed, including grayscale conversion, standardized lighting, and resizing.

  

Model Architecture:

• YOLOv8 : The YOLOv8 model is utilized for real-time object detection. It employs the Darknet-53 architecture and predicts bounding boxes, object scores, and class scores at multiple scales.
• RCNN : Region-based Convolutional Neural Networks are used for fine-grained localization and recognition of traffic signs.

Training:

• The models are trained on Google Colab, leveraging GPU acceleration for faster training.
• The training process involves iterating over 15,000 epochs with an average loss of 0.5 and a learning rate of 0.001.
• Weights are saved periodically during training for further testing and evaluation.

Annotation:

Bounding box annotations for traffic signs are generated using the OpenLabeling tool. Annotations are stored in TXT files following a specific format.