Synthesis AI Solutions for the Public Sector
Object Recognition
Synthetic data can be used to simulate a wide range of objects and scenarios, such as vehicles, weapons, narcotics, explosives, suspicious packages, and equipment in different environments and lighting conditions. For autonomous vehicles, aircraft, watercraft, UAVs, and other equipment, synthetic data can be used to train ML models for object detection, target tracking, threat detection, situational awareness, and obstacle avoidance, as well as for navigation and flight control.
Operator State Monitoring
ML models can be trained to monitor the physical and emotional states of human operators, drivers, and pilots in a wide range of applications. Vary operator demographics, head pose, emotion, and gestures to model real-world situations accurately and detect signs of drowsiness, distraction, impairment, and other performance inhibitors.
Complex Sensor Modeling
Synthetic data enables ML models to be trained on a wide range of optical sensors, without the need for physical access or control of multiple sensor systems or locations. Model multi-modal camera systems (RGB, NIR, thermal, X-ray, etc) in different positions in any vehicle, equipment, location, or environment. Camera placement and sensor specifications are fully tunable.
Biometric Identification
Train perception systems to identify individuals or groups of individuals in a broad range of applications, including physical and virtual access controls, site security, and threat detection scenarios. Facial recognition, gait analysis, and iris and retina recognition are ML tasks that can be used individually or in combination for biometric identification.