IRJET - Multi-Label Road Scene Prediction for Autonomous Vehicles using Deep Neural Networks

This document proposes a multi-label neural network for road scene recognition in autonomous vehicles. It introduces a large-scale dataset called Driving Scene, which contains over 110,000 images across 52 road scene classes. The challenges of this dataset include multi-class prediction, data imbalance with varying image resolutions. The proposed neural network architecture incorporates both single-label and multi-label classification to address data imbalance. It utilizes a deep data integration strategy based on AdaBoost to focus training on minority classes and misclassified samples. Additionally, lane detection and lane departure warning systems are included to provide more context for autonomous driving. The network is trained and evaluated on the Driving Scene dataset to recognize road scenes.