Deep Neural Networks: Improved Object Detection for Autonomous Vehicles

Deep neural networks may have the potential to revolutionize the way an autonomous vehicle perceives the world through methodologies such as object detection. This will require the interaction of neural networks of different kinds to equip the vehicle with capabilities to identify, for example, signage, images, and other entities associated with vehicles, thereby improving transport efficiency. According to many researchers, deep neural networks do not provide varied performance and low accuracy in identification of images. The study shows challenges that number mock image recognition neural networks thus trying to fill up the gap of integration of different neural networks with an autonomous vehicle system. General methods of the study consist of careful observation of the already existing literature, comparative analysis of deep learning networks, and interpretive figures and tables. As much of the study goes on, so many findings about how autonomous vehicles are added to neural networks for deception detection and optimization through the merging of different deep networks have emerged. Finally, the outputs of this paper constitute one of the very critical aspects of the automotive industry, the technology area of sensors, and artificial intelligence.