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Publication
Fusion flow-enhanced graph pooling residual networks for unmanned aerial vehicles surveillance in day and night dual visions
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Advisor(s)
Abstract(s)
Recognizing unauthorized Unmanned Aerial Vehicles (UAVs) within designated no-fly zones throughout the day and night is of paramount importance, where the unauthorized UAVs pose a substantial threat to both civil and military aviation safety. However, recognizing UAVs day and night with dual-vision cameras is nontrivial, since red-green-blue (RGB) images suffer from a low detection rate under an insufficient light condition, such as on cloudy or stormy days, while black-and-white infrared (IR) images struggle to capture UAVs that overlap with the background at night. In this paper, we propose a new optical flow-assisted graph-pooling residual network (OF-GPRN), which significantly enhances the UAV detection rate in day and night dual visions. The proposed OF-GPRN develops a new optical fusion to remove superfluous backgrounds, which improves RGB/IR imaging clarity. Furthermore, OF-GPRN extends optical fusion by incorporating a graph residual split attention network and a feature pyramid, which refines the perception of UAVs, leading to a higher success rate in UAV detection.
A comprehensive performance evaluation is conducted using a benchmark UAV catch dataset. The results indicate that the proposed OF-GPRN elevates the UAV mean average precision (mAP) detection rate to 87.8%, marking a 17.9% advancement compared to the residual graph neural network (ResGCN)-based approach.
Description
This work was supported by the CISTER Research Unit (UIDP/UIDB/04234/2020) and project ADANET (PTDC/EEICOM/3362/2021), financed by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology). Also, this article is a result of the project NORTE-01-0145-FEDER-000062 (RETINA), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).
Keywords
Unmanned aerial vehicles surveillance Residual convolutional networks Split attention network Optical flow fusion
Citation
Alam Noor, Kai Li, Eduardo Tovar, Pei Zhang, Bo Wei, Fusion flow-enhanced graph pooling residual networks for Unmanned Aerial Vehicles surveillance in day and night dual visions, Engineering Applications of Artificial Intelligence, Volume 136, Part B, 2024, 108959, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2024.108959
Publisher
Elsevier