未來展望

針對實驗方法與結果，整理出以下心得作為改善方向：

現今研究已發展出許多基於卷積神經網路的 One Stage 目標分割方法，改用 One Stage 目標分割方法配合Squeezenet、Mobilenet、Shufflenet 等，藉由新穎的目標分割技術能應用 在錶盤偵測上，希望能以維持準確率與效能的情況下，獲得更好的錶盤偵測泛化性。

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自 傳

郭冠毅，1994 年出生於臺北市。

l 台北市南湖國小 l 台北市立明湖國中 l 臺北市立內湖高工

l 私立亞東技術學院電機工程系

l 國立臺灣師範大學電機工程學系研究所

學 術 成 就

l Cheng-Hung Lin and Kuan-Yi Kuo, "A Cost-Effective Automatic Dial Meter Reader Using a Lightweight Convolutional Neural Network", in Proc. of IEEE International Conference on Human System Interaction (IEEE HSI 2020), Tokyo, Japan, 6-8 June, 2020.