較費時的影像處理常常嚴重影響到電腦視覺服務的即時性。近幾年隨著人工智慧物聯網 (AIoT) 的快速發展，結合邊緣計算與AIoT的攝影機 (以下簡稱邊緣攝影機) 已在物件偵測、物件辨識等應用領域獲得成功。雖然近年來已有研究透過深度神經網路進行即時影像辨識，但由於既有研究模型計算過於複雜，無法在資源有限的邊緣攝影機上即時完成任務。所以目前已經有頻域結合時域的深度網路模型來改善上述問題，但目前尚未有純頻域的輕量深度網路。因此，本研究提出「輕量化純頻域深度網路模型」，能夠於邊緣攝影機完成即時影像辨識運算並節省能源。本研究首先優化既有研究「頻域卷積層」以改善提取特徵時產生的偏差，並分別優化「頻域激勵函式」來改善時域激勵函式對頻譜數值造成的準確度下降、「頻域池化層」來改善池化時造成的特徵遺失、「頻域Dropout」來改善不同影像產生數值浮動的問題、「計算域轉換方法」來改善因輸入影像尺寸增大造成模型計算次數度隨之增加的問題。更重要的是，本研究首度提出「頻域全連結層」，其更能表達頻譜資料的特徵分佈關係。實驗結果顯示，進行MNIST與CIFAR-10資料集推論時分別有最高99.74%與99.82%準確度，略勝既有頻域的結果。本研究與既有研究相比，在邊緣攝影機 (採NVIDIA Jetson TX2) 上能分別提升52.01%與52.00%每秒幀率。由以上可知，本研究於即時影像處理與辨識準確度皆擁有絕對優勢。在資料空間處理上，進行上述兩種資料集推論時分別節省最多43.64%與40.00%記憶體使用量。在能源消耗方面，進行上述兩種資料集推論時能分別節省最多26.09%與25.56%每秒電力消耗。綜合上述可說明本研究提出的輕量化純頻域深度網路與過去研究相比更能獲得更佳的辨識度與效能表現。

Smart cameras leveraging edge computing (hereinafter referred to as edge cameras) have been successful in object detection and object recognition. Although there have been research on real-time image recognition through deep neural networks in recent years, their models are too complex to execute in real time on edge cameras with limited resources. Therefore, there are already frequency domain combined with time domain (or hybrid domain) deep network models to improve the above problem, but there is no pure frequency domain lightweight deep network yet. Thus, our study proposes "Lightweight Pure Frequency-Domain Deep Network" that can perform real-time image recognition on edge cameras and save energy. Comparing to the existing time-domain or hybrid domain research, this study optimizes frequency-domain convolutional layer to avoid the bias generated when extracting features, frequency-domain activation function to improve the accuracy, frequency-domain pooling layer to reduce feature loss, frequency-domain dropout to improve the problem of numerical fluctuations in different images, and computational domain transform to reduce the number of calculation increases due to increasing the input image size. More importantly, our study is the first to propose frequency-domain fully connected layer, which can better represent a spectral feature distribution. The experimental results show that the highest accuracy of 99.74% and 99.82% can be achieved when tested using the MNIST and CIFAR-10 datasets, slightly outperform those in the time or hybrid domain. Compared with the existing research, this study can improve the frame per second by 52.01% and 52.00% respectively on the edge camera (on the NVIDIA Jetson TX2). This study saves up to 43.64% and 40.00% memory usage and 26.09% and 25.56% power consumption when tested using the above two datasets respectively.

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