Multi Modal UAV Detection

Multi-modal UAV detection focuses on reliably identifying and tracking unmanned aerial vehicles using diverse sensor data, including visual imagery, LiDAR, radar, and audio. Current research emphasizes improving the accuracy and robustness of detection algorithms, particularly in challenging conditions like varying scales, occlusions, and adverse weather, often employing deep learning architectures such as YOLO variants and novel complementary learning networks. This field is crucial for enhancing safety and security applications, such as airspace management and surveillance, by providing more accurate and comprehensive UAV monitoring capabilities.