本論文旨在應用深度學習辨識輸入影像中的彩色球，並提出撞球策略以比
較不同目標球的入袋難易度。本文將一網路攝影機安裝於機械臂終端，並在攝
影機下方安裝一氣壓缸作為撞球桿，使用影像回受控制校正目標球、母球以及
球桿終點之直角坐標位置。本文首先比較以同樣的訓練資料所訓練出來的兩個
目標辨識模型Yolo v3 以及Mask R-CNN 在撞球影像辨識上的成效。因Mask RCNN
之召回率較高且影像座標位置的誤差較小，故使用Mask R-CNN 檢測輸入
影像中的彩色球。為得到入袋難度最低的目標球，本文提出撞球策略用以分析
所有子球之直射球或組合球的入袋難易度。最後將1 或2 顆子球隨機擺放在撞
球桌上，使用直射球或組合球將目標球撞擊入袋，重複執行155 次可發現入袋
成功時的評估函數通常大於入袋失敗時的評估函數，因此本文提出之撞球策略
用於判斷入袋難易度是十分可靠的。

The objective of this study is to detect color balls in an input image using deep
learning, and to propose a billiard strategy to compare the difficulty of pocketing
different target balls. A webcam is equipped at the end of a manipulator. A pneumatic
cylinder is equipped under the webcam as a cue stick. To align the position of the cue
ball, the target ball, and the end of the cue stick in the cartesian coordinate, a
manipulator is commanded to perform visual alignment. In this study we compare the
performance of two object detection models, Yolo v3 and Mask R-CNN, trained by
the same training data. Since Mask R-CNN has higher Recall and the lower error in
the image position, Mask R-CNN is used to detect color balls in an input image. In
order to get the target ball with the lowest difficulty in pocketing, this study propose a
billiard strategy to analyze the difficulty of using direct or combination shot to pocket
different target balls. Pocketing 1 or 2 color balls randomly placed on the billiard table
and use a direct or combination shot to hit the target ball into the pocket. After
repeating the process 155 times, it can be found that the successful evaluation
functions are usually higher than the failed evaluation functions. Therefore, the
billiard strategy proposed in this paper is very reliable to compare the difficulty of
pocketing different target balls.

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