Multi-altitude, multimodal maritime surveillance system

Markchom, T. ORCID: https://orcid.org/0000-0002-2685-0738, Kourounioti, O., Marturini, M., Bratskas, R., Wohlleben, K., Boyle, J. ORCID: https://orcid.org/0000-0002-5785-8046, Chen, L., Voskopoulos, G., Kontopoulos, C., Veigl, S., Opitz, A., Gkamaris, A., Papachristos, D., Lunic, D., Ferryman, J., Kriechbaum-Zabini, A. and Leventakis, G. (2025) Multi-altitude, multimodal maritime surveillance system. IEEE Sensors Journal. ISSN 1530-437X (In Press)

Abstract/Summary

Maritime surveillance plays a vital role in protecting coastal and maritime environments. However, traditional maritime surveillance systems that rely on single-altitude, single-modality sensors suffer from limited coverage and sensitivity to weather conditions. To address these limitations, this paper presents a comprehensive maritime surveillance system that integrates multi-altitude, multimodal sensor platforms, including ground-based sensors, low-altitude UAVs, and satellites, for maritime threat detection. Each platform is equipped with dedicated modules for object detection, tracking, and geolocation, leveraging its unique sensing capabilities to contribute to a coordinated surveillance system. Moreover, a novel Multi-Altitude, Multimodal Maritime Surveillance (MAMMS) dataset is introduced. This dataset includes data from these sensor types, enabling rigorous benchmarking across varying operational conditions. Experimental results indicate that the system achieved an average mAP of 50.5% across all sensors in object detection, surpassing state-of-the-art models in most cases. For object tracking, the system achieved an average IDF1 of 0.263 and a HOTA of 0.297, comparable to state-of-the-art methods, while exhibiting substantially fewer average ID switches (75.46) compared to the strongest baseline (301.46). For geolocation approximation, the system achieved an error of less than 11m in certain scenarios. A case study was also conducted to assess the sensor platforms when integrated into a multi-sensor fusion system. The case study showed that complementary information from different platforms can help reduce false alarms and improve object geolocation accuracy.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/127545
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	IEEE
Download/View statistics	View download statistics for this item