GPS, thermal, radar, IMU, lidar, industrial sensors, everything is merged directly with image data during training. One model that sees and understands the physical world.
Conventional AI only sees images. PCNN fuses camera data with physical sensor data at the tensor level, not as post-processing, but directly in training. The result: a model with multiple senses.
Camera + GPS + thermal + radar + IMU + lidar + industrial sensors. All simultaneously.
Sensor data is encoded as additional tensor layers and fused during training. Not post-processing.
The model understands not just what it sees, but where, when, and under what physical conditions.
Traffic analysis by fusing camera data with GPS flow data and environmental sensors. Counts, classifies, and tracks vehicles with spatial context.
Aerial object detection by fusing camera data with GPS altitude data and radar signals. Maintains tracking in cloud cover and fog.
Ship detection by fusing camera data with AIS transponders, GPS, and radar data. Distinguishes vessel types and predicts trajectories.
Aircraft tracking by fusing visual detection with ADS-B, radar, and satellite data for airspace monitoring.
Alle Sensorsignale werden als Tensorschichten kodiert und während des Trainings fusioniert — nicht nachträglich. Ein Modell, mehrere Sinne.
KINEVA trains sensor fusion models for your specific signals and environments.