Indian Driving Scene Description Generator Using Deep Learning

Driving scene description generation plays a crucial role in various applications, including driver assistance systems, traffic monitoring and autonomous driving. This study proposes a comprehensive approach for driving scene description generation using deep learning (DL) techniques. The methodology involves the collection and preprocessing of a large-scale driving scene dataset, which consists of diverse driving scenarios captured from dashcam videos. The dataset is carefully labelled for different scene categories, such as highways, urban areas, night scenes, and rural roads. Frames from video datasets are collected and are captioned for DL-Model for various scene. In addition, DL-based approaches are employed to leverage the power of convolutional neural networks (CNNs) for scene detection. The CNN architecture, such as VGGNet-16 is evaluated to extract high-level features and enable accurate scene captioning from recurrent neural network (RNN) architectures such as LSTM and GRU for caption generation. The evaluation of the proposed DL models is done using BLEU score. The scores depict the level of effectiveness of the approach in accurate generation of captions for driving scenes, achieving high performance across various scene categories. The system can be deployed on embedded platforms or integrated into existing driver assistance systems to enhance situational awareness and improve decision-making processes.