本系統致力於結合高解析度 SAR 衛星影像與邊緣運算技術，提供即時且精準的海洋環境監測方案。透過前端互動圖台擷取目標區域，並無縫連接底層的硬體加速架構，我們能在複雜的海象環境中，迅速且準確地完成異常特徵的二元分類，為守護海洋生態提供高效的實務解方。
結合輕量化神經網路與 FPGA 協同設計，在資源受限的邊緣裝置上實現低延遲、高精準的即時海洋環境監測。
本作品致力於解決 SAR (合成孔徑雷達) 影像中的海面油汙辨識挑戰。透過 QKeras 建立適合硬體部署的低位元量化 CNN，並藉由 hls4ml 將模型無縫轉換為 Vitis HLS 可合成之 FPGA IP，大幅縮短模型硬體化的開發週期。輸入的灰階影像經過一般 C++ 環境前處理後，會被精準轉換為依序交錯的 HWC 記憶體排列格式，確保軟硬體間的資料流完美對齊。
在系統整合方面，我們採用 PYNQ-Z2 作為核心平台，透過 PS 端進行 AXI DMA 控制與資料搬移，並由 PL 端負責底層硬體推論。針對滑動視窗擷取下的局部特徵，設計了包含兩層量化卷積與三段池化的極致輕量化架構，以最少的運算資源精準輸出「油汙」與「無油汙」兩種分類結果，突破傳統硬體的效能瓶頸。
專為 PYNQ-Z2 最佳化的軟硬體協同設計
上傳海洋的圖片，我們的將會辨識是否有油汙。
顯示即時影像畫面
直接串接 Sentinel Hub WMS API，提供可拖曳、縮放的 Sentinel-1 灰階雷達圖層。紅色框線為系統預定擷取並送入硬體執行二元分類的 ROI 區域。
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