由於近年來科技的蓬勃發展，自動化機器被廣泛應用於不同的場合，像是產線中的自動化手臂及家庭裡的自動清掃機器等。自動化機器又會因使用方式不同，需在機器上加裝不同的感測器。在眾多感測器中又屬視覺感測器能獲取較為豐富的資訊，因此使用機器視覺與機器控制的整合技術，是現今較為廣泛的發展。自動化停車是以機器視覺搭配其他感測器完成的機器控制，其成效也在近年來成為昂貴車輛的標準配備，但是在辨識度能力不夠以及感測器昂貴的情況下，一般的影像處理已經無法有效處理畫面中的資訊。近年來蓬勃發展的深度學習更是在影像辨識的領域中有不錯的成果，搭配影像處理能夠使視覺得到的資訊更為完整。本研究利用卷積神經網路學習辨識出不同的車位與車輛，並使用影像處理獲取車格資訊以達到自動停車的目的。此外本研究於虛擬環境中，驗證了此整合系統的可行性，在環境中車輛能達到繞場、找車位、停車等動作，並設計更改車位位置、種類或車輛起始點。最後結果驗證了此整合系統良好的效果。

With the vigorous development of technologies in the recent years, automatic machines have been widely used in different occasions, such as automatic arms in production lines and automatic cleaning machines at homes. Different sensors are added to the automatic machines due to different applications. Among the many sensors, visual sensors can obtain more information. Therefore, the integrated technology of machine vision and machine control is a relatively extensive development nowadays. Automatic parking is a machine control method that combines visual sensors. Visual sensors have also become the standard equipment of expensive cars in recent years. However, in the case of insufficient recognition capabilities and expensive sensors, general image processing is no longer efficient enough to process the information on the screen. In recent years, the vigorous development of deep learning has achieved good results in the field of image recognition. With image processing, the information obtained by the vision can be more complete. In this study, convolutional neural networks are used to identify different parking spaces and cars, and image processing is used to obtain information to achieve automatic parking. In addition, this study uses a virtual environment to verify the feasibility of this integrated system. In this simulated environment, the car can achieve the actions of detouring, finding parking space, auto parking, etc. The parking location, type of car and starting point. The final result verifies the good performance of this integrated system.

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