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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.16089 |
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| _version_ | 1866917810673811456 |
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| author | Sakellariou, Nikos Lalas, Antonios Votis, Konstantinos Tzovaras, Dimitrios |
| author_facet | Sakellariou, Nikos Lalas, Antonios Votis, Konstantinos Tzovaras, Dimitrios |
| contents | The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society. This, however, causes an ever-increasing number of reported malicious or accidental incidents, rendering the need for the development of UAV detection and classification mechanisms essential. We propose a methodology for developing a system that fuses already processed multi-sensor data into a new Deep Neural Network to increase its classification accuracy towards UAV detection. The DNN model fuses high-level features extracted from individual object detection and classification models associated with thermal, optronic, and radar data. Additionally, emphasis is given to the model's Convolutional Neural Network (CNN) based architecture that combines the features of the three sensor modalities by stacking the extracted image features of the thermal and optronic sensor achieving higher classification accuracy than each sensor alone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16089 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data Sakellariou, Nikos Lalas, Antonios Votis, Konstantinos Tzovaras, Dimitrios Artificial Intelligence Signal Processing The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society. This, however, causes an ever-increasing number of reported malicious or accidental incidents, rendering the need for the development of UAV detection and classification mechanisms essential. We propose a methodology for developing a system that fuses already processed multi-sensor data into a new Deep Neural Network to increase its classification accuracy towards UAV detection. The DNN model fuses high-level features extracted from individual object detection and classification models associated with thermal, optronic, and radar data. Additionally, emphasis is given to the model's Convolutional Neural Network (CNN) based architecture that combines the features of the three sensor modalities by stacking the extracted image features of the thermal and optronic sensor achieving higher classification accuracy than each sensor alone. |
| title | Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data |
| topic | Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2410.16089 |