Continuous Wave (FMCW) radars represent the most Unmanned Aerial Vehicles (UAV) have successfully attractive and cost-efficient solutions [2]. While for the permeated modern society with various applications for civil verification and classification task various methods exist in and military purposes. Oil and gas, construction, metals and literature employing machine learning techniques such as mining already incorporate UAVs in their processes. SVM [3], Random Forests [4], Nearest Neighbor [5] and Furthermore, UAVs are employed for commercial purposes, Deep Neural Networks [6][7][8]. More recent DNN such as the monitoring of public places, cartography, survey approaches based on convolutional neural networks are wildlife, search and rescue (SAR), first aid and delivery of introduced in Samaras et al. [9]. The authors presented a deep goods. Big technological companies continuously challenge learning classification method based on data from an X-band the status quote by announcing breakthrough services. FMCW surveillance 2D radar that is able to reach a Moreover, progress in UAV regulation has driven classification accuracy of up to 95.0% utilizing a custom investments since 2019, to further increase the popularity and CNN based architecture. A similar approach is presented in use of UAVs in sectors that present significant potential but [10] where the authors proposed Res-Net-SP, a compressed still minimal use, such as agriculture, healthcare, architecture of ResNet-18 that is based in convolutional infrastructure, property management and insurance.

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.