This project implements a UNet-based Convolutional Neural Network (CNN) architecture using PyTorch to segment highway lanes from input images. The segmentation of highway lanes is a critical task in autonomous driving systems and road safety applications, enabling vehicles to navigate safely and efficiently. By leveraging deep learning techniques, the model learns to accurately delineate lane boundaries, contributing to enhanced road safety and advanced driver assistance systems (ADAS).
The architecture utilizes the UNet framework, known for its effectiveness in semantic segmentation tasks. It consists of an encoder-decoder structure, where the encoder extracts features from the input image, and the decoder generates a pixel-wise segmentation mask. PyTorch, a popular deep learning framework, is employed for implementation, providing flexibility and ease of use in model development.
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Training:
- Prepare a dataset containing images annotated with ground truth lane segmentations.
- Train the UNet model using PyTorch, optimizing a predefined loss function such as cross-entropy or dice loss.
- Iterate through training and validation phases to improve segmentation accuracy.
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Evaluation:
- Evaluate the trained model's performance using a separate validation dataset.
- Utilize metrics such as Intersection over Union (IoU) or pixel accuracy to quantify segmentation accuracy.
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Deployment:
- Once trained and validated, the model can be deployed to segment highway lanes from unseen images efficiently.
- Integrate the segmentation capability into various applications, such as autonomous driving systems or traffic monitoring systems, to enhance functionality and safety.
- Optimize the model for real-time performance to enable seamless integration into autonomous vehicles.
- Explore additional datasets to improve model generalization and robustness.
- Enhance the architecture to incorporate advanced features and improve segmentation accuracy further.
- Ankith Reddy Avula
This project is licensed under the Apache License 2.0.