隨著物聯網 (IoT) 的發展，結合人工智慧的邊緣計算攝影機 (以下簡稱邊緣攝影機) 已能夠直接在邊緣端進行影像強化。近年來，已有研究採用深度神經網路進行空拍影像除霧。然而，既有的研究大多僅使用RGB色彩空間的影像進行除霧，或是需要額外使用其他影像來源進行除霧，未考慮不同的色彩空間中蘊含的特徵，導致無法充分利用既有的輸入。為了獲取更豐富的特徵，本研究首度提出「輕量化多色彩空間特徵融合空拍影像除霧網路」，將輸入影像透過色彩空間轉換來額外產生HSV以及YCbCr色彩空間，並使用特徵注意力模組將不同色彩空間的特徵進行融合並給予加權，使模型更加關注在重要的特徵。同時，使用了邊線增強模組，使特徵在模型裡傳遞的時候能最大化的保留邊線的資訊。實驗結果顯示，相較於最新研究，在Sate1K資料集中，未輕量化的網路平均使PSNR提升1.57%，SSIM提升3.48%，而輕量化的網路平均使PSNR提升0.11%，SSIM提升3.25%。在RICE資料集中，未輕量化的網路使PSNR提升5.00%，SSIM提升0.78%，輕量化的網路使PSNR提升3.49%，SSIM提升0.71%，因此可以證明本研究提出之網路可獲得品質更好的空拍除霧影像。接著，為了充分利用攝影機的運算資源，且避免對無霧的空拍影像進行不必要的處理，本研究加入「輕量化動態空拍影像除霧偵測網路」，該模型用於評估空拍影像中是否存在霧，以決定是否需要除霧。實驗結果顯示，將輕量化動態影像除霧偵測網路與輕量化多色彩空間特徵融合空拍影像除霧網路整合後，在空拍影像有霧的占比為0.2時，能使FPS提升218.90%、平均運行時間降低68.35%、PSNR提升196.15%以及SSIM提升4.69%。當占比為0.4時，能使FPS提升92.47%、平均運行時間降低47.44%、PSNR提升141.20%以及SSIM提升3.40%。當占比為0.6時，能使FPS提升35.24%、平均運行時間降低25.97%、PSNR提升91.11%以及SSIM提升2.27%。當占比為0.8時，能使FPS提升4.43%、平均運行時間降低5.13%、PSNR提升44.73%以及SSIM提升1.11%。因此可以證實動態評估影像可提高邊緣攝影機的運行效率以及輸出的影像品質。

With the development of the Internet of Things (IoT), smart cameras combined with artificial intelligence (AI) and edge computing technologies (hereafter referred to edge cameras) improve image quality directly on edge cameras. In recent years, deep neural networks have been employed for aerial image dehazing. However, existing research mainly focused on using RGB images or required additional images, neglecting the potential features in different color spaces and thus failing to fully utilize the input images. To address this, a novel "lightweight multi-color space feature fusion aerial image dehazing network" is proposed in this study. The input image is transformed into HSV and YCbCr color spaces, and a feature attention block is utilized to fuse and weight the features from different color spaces, enabling the model to focus more on crucial features. Additionally, a borderline boosting block is utilized to maximize the preservation of borderline details during feature propagation within the model. Experimental results demonstrate improvements over the latest research. In the Sate1K dataset, the non-lightweight network increases peak signal-to-noise ratio (PSNR) by 1.57% and structural similarity index (SSIM) by 3.48%, while the lightweight network increases PSNR by 0.11% and SSIM by 3.25%. On the RICE dataset, the non-lightweight network increases PSNR by 5.00% and SSIM by 0.78%, while the lightweight network increases PSNR by 3.49% and SSIM by 0.71%. These results validate that the proposed network can improve the quality of the dehazed aerial images. Furthermore, in order to effectively utilize the computational resources of edge cameras, this study introduces a "lightweight dynamic aerial image dehazing detector". This model first detects haze in aerial images to determine if dehazing is needed. Experimental results show that integrating this detection model with the dehazing network significantly improves performance. When the proportion of haze aerial images is 0.2, it increases the frames per second (FPS) by 218.90%, reduces the average processing time by 68.35%, increases PSNR by 196.15% and increases SSIM by 4.69%. At a proportion of 0.4, it increases FPS by 92.47%, reduces average processing time by 47.44%, increases PSNR by 141.20% and increases SSIM by 3.40%. At a proportion of 0.6, it increases FPS by 35.24%, reduces average processing time by 25.97%, increases PSNR by 91.11% and increases SSIM by 2.27%. At a proportion of 0.8, it increases FPS by 4.43%, reduces average processing time by 5.13%, increases PSNR by 44.73% and increases SSIM by 1.11%. This confirms that dynamically evaluating the image condition can improve the operational efficiency of edge cameras and the quality of output images.

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