This project focuses on image segmentation for self-driving cars using the U-Net architecture. The U-Net is a convolutional neural network designed for image segmentation tasks, making it suitable for identifying and localizing objects in real-time. The model is trained on a dataset of labeled images containing various objects relevant to autonomous driving, such as vehicles, pedestrians, and traffic signs. The goal is to develop a robust model to segment different features of the road to enhance the safety and performance of self-driving cars.
Obtain a dataset of labeled images with annotations for objects of interest (e.g., vehicles, pedestrians, traffic signs) relevant to self-driving cars.
Resize images to a consistent resolution suitable for model input. Normalize pixel values to a standardized range (e.g., 0 to 1). Augment the dataset using techniques like rotation, flipping, and scaling to improve model generalization.
Apply data augmentation techniques such as rotation, flipping, scaling, and random cropping to increase the diversity of the training dataset and improve model robustness.
Train the U-Net model using the augmented training dataset.
Evaluate the trained model's performance using metrics such as Intersection over Union (IoU) and Mean Average Precision (mAP) on a validation dataset.
The U-Net architecture consists of an encoder-decoder structure with skip connections. It efficiently captures both local and global features, making it suitable for image segmentation tasks like object detection.