  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.
