Efficient Multi-task Learning for Road Scene Classification: Scene, Time, and Weather Predictions

This paper addresses the task of road scene classification by leveraging multi-task learning (MTL) to improve model performance and computational efficiency. We employ the BDD100K dataset, which provides diverse driving scenarios, and apply data augmentation and balancing techniques to mitigate class imbalances. Our methodology integrates a ResNet18 backbone pre-trained on ImageNet, extended with task-specific heads for scene type, time of day, and weather classification. Uniform Manifold Approximation and Projection (UMAP) is used for dimensionality reduction and visualization of feature embeddings. Experiments demonstrate that our MTL approach significantly enhances classification accuracy across tasks, with notable performance in time-of-day classification. The proposed model achieves robust results despite class imbalances, validated through confusion matrices and UMAP visualizations. Our findings highlight the benefits of MTL in road scene classification, offering insights for future research in autonomous driving and computer vision.