融合光流增强的图池化残差网络用于昼夜双视觉无人机监控
Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visions
AI总结：
针对昼夜双视觉下无人机检测率低的问题，提出光流辅助图池化残差网络（OF-GPRN），通过光流融合去背景、图残差注意力与特征金字塔增强感知，在基准数据集上mAP达87.8%，较ResGCN提升17.9%。
摘要：
在指定禁飞区内全天候识别未经授权的无人机（UAV）至关重要，因为未经授权的无人机对民用和军用航空安全构成重大威胁。然而，使用双视觉相机昼夜识别无人机并非易事，因为红绿蓝（RGB）图像在光照不足的条件下（如阴天或暴风雨天）检测率较低，而黑白红外（IR）图像则难以捕捉在夜间与背景重叠的无人机。本文提出了一种新的光流辅助图池化残差网络（OF-GPRN），显著提高了昼夜双视觉下的无人机检测率。所提出的OF-GPRN开发了一种新的光流融合技术来去除多余背景，从而提高了RGB/IR成像的清晰度。此外，OF-GPRN通过引入图残差分裂注意力网络和特征金字塔扩展了光流融合，细化了对无人机的感知，从而提高了无人机检测的成功率。使用基准无人机捕获数据集进行了全面的性能评估。结果表明，所提出的OF-GPRN将无人机平均精度（mAP）检测率提升至87.8%，相比基于残差图神经网络（ResGCN）的方法提高了17.9%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
