Summary of Multi-sensor Fusion For Uav Classification Based on Feature Maps Of Image and Radar Data, by Nikos Sakellariou (1) et al.
Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data
by Nikos Sakellariou, Antonios Lalas, Konstantinos Votis, Dimitrios Tzovaras
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology develops a system for detecting and classifying unmanned aerial vehicles (UAVs) using deep neural networks. The approach fuses processed multi-sensor data from thermal, optronic, and radar sources to improve classification accuracy. A convolutional neural network (CNN) architecture combines features extracted from individual object detection and classification models, outperforming each sensor modality alone. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are working on a way to recognize and classify drones using special computer programs that combine information from different sensors like heat cameras, normal cameras, and radar. They’re trying to make it better by joining all the data together and using a special type of AI called convolutional neural networks (CNN). The goal is to improve how well the system can tell if something is a drone or not. |
Keywords
» Artificial intelligence » Classification » Cnn » Neural network » Object detection
