無人機因其易於部署和鳥瞰視角而常用於監控和交通管理。車牌偵測和識別是交通監控應用中常見的問題。本研究首先設計基於YOLOX的車輛偵測（YOLOX-VD），其輸出的兩個目標車輛的邊界框通過另一個基於YOLOX的車牌偵測（YOLOX-LPD）進行訓練。由於車牌距離較遠（例如40m），通過生成對抗網絡（GAN）的超解析度技術以提高其解析度，從而將其輸出的邊界框通過修改的車牌識別網絡（MLPRN）進行訓練，以提升車牌識別的能力。之後，以核化相關濾波器（KCF）預測特定車輛在YOLOX-VD階段的邊界框。最後，以遠程4G網絡的六旋翼機實現包括YOLOX-VD，YOLOX-LPD，GAN-ESR，MLPRN和KCF-VT的集成機器學習，進行往返超過2500m的公路上特定車輛之監視和追蹤。

Abstract---Characterized by their ease of deployment and bird’s-eye view, unmanned aerial vehicles are often for surveillance and traffic management. License plate detection and recognition are common problems in traffic surveillance applications. In this work, the YOLOX-based vehicle detection (YOLOX-VD) is first designed such that its output bounding boxes of two target vehicles are trained by another YOLOX-based license plate detection (YOLOX-LPD). Since the license plates are in a far distance (e.g., 40m), the super-resolution by generative adversarial network (GAN) increases their resolutions such that their output bounding boxes are trained by the modified license plate recognition network (MLPRN) to enhance license plate recognition. After that, Kernelized Correlation Filter (KCF) predicts the bounding box of the specific vehicle in the YOLOX-VD stage. Finally, the integrated machine learning, including YOLOX-VD, YOLOX-LPD, GAN-ESR, MLPRN, and KCF-VT, is implemented by a remote 4G networking hexa-rotor to surveil and track the specific vehicle on a highway with a round trip over 2500m.

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