Perception Network

Perception networks are artificial neural networks designed to interpret sensory data, primarily visual information, for tasks like object detection, scene understanding, and autonomous navigation. Current research emphasizes improving robustness, efficiency, and safety, focusing on architectures like multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs), often integrated with techniques like teacher-student learning and model predictive control (MPC) for enhanced performance and reliability. These advancements are crucial for applications ranging from medical image analysis and robotics to autonomous vehicles, where reliable and efficient perception is paramount for safe and effective operation.