本論文以實際應用於RoboCup國際性機器人競賽為目的，提出一使用深度學習模型之雙足人形足球機器人機器視覺系統，達到機器視覺影像對足球與機器人認知，以及足球追蹤之目的。RoboCup國際機器人競賽舊制規定，競賽場地範圍內採用綠色為底色，場地上著有白色場域線，競賽中兩隊伍機器人著有紅色或藍色標帶，配合使用橘色足球與黃色球門進行比賽。因此，往年競賽中，與賽隊伍主要採取物件顏色之區別，作為主要目標物辨識之特徵。然而，隨著賽事規則逐年更動，比賽場地逐步轉化為一般足球賽之場景，2015年開始將足球更改為白色帶花紋之標準比賽用球，球門框顏色亦由黃色框變更為白色框，大幅度增加機器人辨識上之複雜度。為了解決上述機器人在交錯白線中能夠識別足球，降低賽前所需的參數調整，以及增加未來賽中物件更動的適應彈性，本論文之機器視覺系統以深度學習卷積神經網路開發，透過線下監度學習方式，使用You Only Look Once第二版的網路結構進行學習，達到足球類別與機器人類別在複雜環境中辨識之框選結果。此外，透過實驗深度學習模型框選足球區域面積與實際足球佔有影像區域面積數據，進行深度學習模型訓練之成效比較，並測試出框選準確度較佳之數據模型，經由機器人足球影像座標，將足球實際所在位置，轉換為機器人內部座標系定義，完成機器人在複雜場景或比賽場景中，足球影像之定位與追蹤實現，並於RoboCup 2017賽事中取得人形機器人足球賽第二名成績。

　　This thesis proposes a deep learning based machine vision system for biped humanoid soccer robot. This system was developed for RoboCup international robotic competition to recognize objects belonging to two classes namely soccer ball and various humanoid robots. This machine vision system had to track the soccer ball automatically. In the RoboCup humanoid soccer event, field lines were white in color, soccer ball was white in color and the goal was white in color and the soccer field was green in color. Robots of two teams were differentiated with red and blue tapes. Since both the soccer ball and the field lines were white in color, it was very difficult to differentiate between the soccer ball and field lines when the ball was near the field lines by using conventional image processing methods. Hence, a deep neural network was used for recognizing the soccer ball in the field and to generate a bounding box around it. You Only Look Once is a deep learning object recognition algorithm used in this system. The predicted bounding box from deep learning model was collected to be compared with the ground truth data. This enabled to obtain more accurate results regarding the location of the soccer ball. The bounding box coordinates were used to compute the floor coordinates of the soccer ball with respect to the robot. Finally, this system had been tested in RoboCup 2017. Based on the proposed approaches, our team received the second place of the teen-size humanoid soccer game.

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