無人機可以為智慧城市的發展提供高效的解決方案。除了遠程控制多架無人機，近場和遠距離的視覺辨識和導引到可能的靜態、動態降落平台以進行無線充電或與地面車輛協作是至關重要的。為了滿足遠距離的辨識精度要求，減少邊緣運算的計算時間，並且降低功耗以增加飛行距離，首先針對特定降落平台（SLP）做訓練然後以基於可調整關注的YOLOX（AA-YOLOX）測試。為了能成功且精確地降落在 SLP，需要附加一個標記來幫助降落操作。在此情況下，本研究還實現了帶有標記的 SLP 的少樣本訓練，以提高識別率的魯棒性。此外針對有標記和沒有標記的SLP的情況分別測試了兩個學習權重。最終，在各種場景下的實時戶外驗證近距離及遠距離的大型無人機視覺導引，包括不同的背景和天氣條件，以及SLP有部分遮擋，證實我們方法的可行性、穩健性和實用性。

Unmanned aerial vehicles (UAVs) can provide efficient and effective solutions for the development of smart cities. Besides the remote control of multiple UAVs, near-field visual recognition and guidance to a possible dynamic landing platform for wireless charging or collaboration with ground vehicle is paramount. To satisfy the required recognition accuracy from a far distance, the reduction of computation time for edge computing, and the attenuation of power consumption for increasing flight range, an Adjusted-Attention based YOLOX (AA-YOLOX) is first trained and tested for specific landing platform (SLP). To successfully land an SLP, a marker is attached to help the landing operation. In this situation, few-shot training for SLP with marker is also achieved to increase the robustness of recognition rate. Furthermore, two learning weights respectively for SLP w/o and with maker are tested for situations of SLP w/o and with marker. Ultimately, online outdoor verifications in various scenarios, including different background and weather conditions, and partial occlusion, confirm the feasibility, robustness, and practicality of our approach.

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