Project Title: Traffic Lights Detection and Classification using ResNet50
Project Description:
In the realm of autonomous driving and intelligent transportation systems, the ability to accurately detect and classify traffic signals is crucial for ensuring safety and compliance with traffic regulations. This project seeks to develop a robust system for traffic light detection and classification using the ResNet50 architecture, a state-of-the-art convolutional neural network (CNN) known for its depth and ability to handle complex image patterns.
Objectives:
1. Traffic Light Detection: Identify and locate traffic lights in real-time video feeds or images.
2. Traffic Light Classification: Classify the detected traffic lights into three categories: Red, Yellow, and Green.
3. Real-time Application: Implement the model for real-time analysis to assist autonomous vehicles in decision-making processes.
Methodology:
1. Data Collection:
– Gather a diverse dataset of traffic light images from various environments and conditions (day/night, rainy/sunny, urban/rural).
– Utilize publicly available datasets such as the LISA Traffic Light Dataset or create a custom dataset by capturing images from a vehicle-mounted camera.
2. Data Preprocessing:
– Annotate the dataset with bounding boxes around the traffic lights.
– Split the data into training, validation, and test sets.
– Apply augmentation techniques (rotation, scaling, flipping) to increase dataset diversity and improve model robustness.
3. Model Architecture:
– Leverage the ResNet50 architecture, which includes 50 layers, skip connections, and deep residual learning, providing excellent feature extraction capabilities.
– Fine-tune the model using transfer learning; pre-train on a large image dataset (e.g., ImageNet) and adapt the top layers for traffic light detection and classification tasks.
4. Training Process:
– Implement training using a dataset that is split into training and validation sets.
– Use appropriate loss functions (e.g., categorical cross-entropy for classification) and metrics (e.g., accuracy, F1-score).
– Experiment with different hyperparameters (learning rate, batch size) to optimize model performance.
5. Evaluation:
– Test the model on a separate test dataset to evaluate its accuracy, precision, recall, and F1 score.
– Analyze misclassifications and refine the model accordingly.
– Implement cross-validation techniques to ensure reliability.
6. Real-time Implementation:
– Integrate the trained model into a real-time video processing pipeline using OpenCV and TensorFlow/Keras.
– Optimize the model for faster inference times, ensuring it can run effectively on resource-constrained devices such as embedded systems or edge devices.
Expected Outcomes:
– A fully functional traffic light detection and classification system capable of accurately identifying and classifying traffic lights in various conditions.
– A comparative analysis of the model’s performance against existing methods and algorithms.
– Documentation detailing the implementation process, challenges faced, and solutions implemented.
Future Work:
– Expand the model to recognize additional traffic signs and signals for a more comprehensive intelligent driving system.
– Incorporate advanced techniques such as YOLO (You Only Look Once) or SSD (Single Shot Detector) for improved real-time object detection capabilities.
– Enhance the model’s robustness by incorporating reinforcement learning algorithms for dynamic environment adaptation.
Conclusion:
Traffic light detection and classification using ResNet50 represents a vital step towards advancing autonomous vehicle technologies and improving traffic safety. By harnessing deep learning techniques and real-time processing capabilities, this project aims to contribute significantly to the development of intelligent transportation systems, paving the way for smart cities and safer roads.
