Laboratory for the training and inference of artificial intelligence models for artificial vision applications

The SMART LAB project has established a dedicated laboratory for training and inference of advanced perception systems, focusing on the research and development of new algorithms and sensors designed to capture and interpret the environment. These systems, referred to as “Intelligent Perception Systems,” aim to replicate aspects of human perception in complex scenarios. The laboratory provides the infrastructure and tools needed to design, test, and optimize these systems, supporting experiments in real and simulated environments. Central to the project is the development of Deep Learning techniques capable of detecting objects, interpreting dynamic scenes, and understanding interactions within the environment. This involves creating models that can process high-dimensional sensor data, including visual, lidar, and radar inputs, and make accurate predictions about the state of the surroundings. By integrating these algorithms with advanced sensors, the SMART LAB enables a seamless flow from data acquisition to actionable insights. The laboratory also supports the evaluation and benchmarking of models to ensure robustness and reliability under diverse conditions. Researchers can iterate on algorithm design, test new sensor configurations, and refine inference methods to improve performance. The project emphasizes scalability and modularity, allowing new models and sensors to be incorporated efficiently. Additionally, the laboratory serves as a platform for collaboration between teams working on perception, machine learning, and robotics. By bridging the gap between algorithmic development and practical sensor deployment, SMART LAB accelerates innovation in the field of autonomous systems and intelligent perception. The ultimate goal is to create systems that interpret complex scenes as a human would, enhancing safety, situational awareness, and decision-making in real-world applications.