近年來，影像辨識與深度學習逐漸應用於物流業、零售業等等領域，尤其是隨著服務的需求提高，將其導入市場來協助店鋪營運與提升消費者消費的便利性，而這些可節省人力之影像辨識應用也是未來發展的趨勢。本研究以製作自動翻轉取像機構並將影像處理與深度學習應用於商品辨識。其分為三個階段:第一階段為自動翻轉取像系統，在此階段製作出可自動旋轉、翻轉商品並拍攝之機構，而取像商品則選用不同外觀特徵，如形狀、顏色等等進行取像，以5類19個商品的不同姿勢拍攝約可得到20,392張影像；第二階段為影像前處理，將第一階段取得之所有影像進行影像前處理，可自動得到含有商品在影像中位置資訊的標記檔，內容包括商品標籤名稱之索引及商品相對於影像之位置比例，每處理一張並產生標記檔約需6.5秒；第三階段為進行兩版本YOLO演算法來訓練與測試商品辨識結果，發現在前面階段得到的單一影像集中在加入少量手動標註的複雜影像，即可使YOLOv4訓練模型的測試集mAP達到95.44%。根據實驗結果可知，本論文所設計之自動翻轉取像機構可以節省收集訓練資料影像集的人力及減少時間成本，並且還可使訓練模型對商品的辨識精確度有良好的影像辨識效果。

In recent years, image recognition and deep learning has gradually been applied to the logistics industry, and retail industry etc. Especially as the demand for services increases, it is introduced into market to assist store operations and improve the convenience of consumer consumption. Manpower-saving image recognition applications are also the trend of future development. This research is to make an automatic flip imaging mechanism. Then, image processing and deep learning are applied on product recognition. It is divided into three stages: The first stage is an automatic flip imaging system, in which a product can be automatically rotated, flipped and photographed. The sampled products are imaged with different appearance features, such as shape, color, etc. Approximate 20,392 images can be obtained by shooting 19 samples from 5 kinds of products with different posture. The second stage is image pre-processing for images obtained in the first stage. All the images can be automatically labelled. The position information of the product, including the index of the product label name and the position ratio of the product relative to the image will be recorded. Each processed one will generate a label file. It takes 6.5 seconds for each image. The third stage is to train two versions of YOLO algorithms and test the product identification results. It is found that the single image obtained in the previous stage is concentrated in a small number of manually annotated complex images, which can make the mAP of test set under the Yolov4 training model to 95.44 %. According to the experimental results, the automatic flip imaging system designed in this thesis can save the labor and reduce time cost of collecting training data image sets. It can also make the training model have a good image recognition effect on the accuracy of product recognition.

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