基於刮痕瑕疵資料擴增的分割拼接影像生成;Split-Generate-stitch generation method for augmentation based on scratch defect data

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題名:	基於刮痕瑕疵資料擴增的分割拼接影像生成;Split-Generate-stitch generation method for augmentation based on scratch defect data
作者:	陳皇翰;Chen, Huang-Han
貢獻者:	資訊工程學系
關鍵詞:	瑕疵檢測;影像擴增;語意分割;電腦視覺;Defect Inspection;Data Augmentation;Semantic segmentation;Computer Vision
日期:	2022-09-22
上傳時間:	2022-10-04 12:08:44 (UTC+8)
出版者:	國立中央大學
摘要:	在生產線上，若要提升產品品質，瑕疵的檢測與篩選是重要的生產流程之一，傳統的瑕疵檢測方式以人力為主，除了較耗費成本外，挑選品質會受到人員的身體狀況以及環境因素等外部原因的影響，因此使用電腦視覺進行瑕疵檢測是近年較普遍的做法。　　使用電腦視覺判斷瑕疵與否的必要條件是需要有一定數量的資料，才能讓準確度保持一定水準，然而瑕疵資料比良品資料的收集難度高，因此使用資料擴增的方法是不可或缺的。目前常見的擴增方式（如平移、翻轉、旋轉等）可能產生不符合真實情況的影像，且瑕疵樣態接近，這種方式對較多樣性的特徵擴增樣態增加量有限。因此本研究提出生成多樣性瑕疵資料的資料擴增方法，將數量有限的真實影像轉換成新樣態的瑕疵資料，以達到擴增瑕疵樣態的目的，另外，為了讓生成的瑕疵結果能夠受到人為的控制，本研究的生成系統中使用語意分割的標記影像方式加入場域知識，然而傳統的語意分割生成模型受到少量資料集的限制，結果並不理想，因此本研究受到文獻啟發，提出以分割拼接的方式針對瑕疵進行生成以生成出品質更好的擴增影像。 ;In the production line, in order to improve product quality, defect detection and screening is one of the important production processes. The traditional defect detection method is mainly labor-based. In addition to the high cost, the selection result will be affected by the physical condition of the personnel and environmental factors. Due to the influence of external factors, the use of computer vision for defect detection has become a more common practice in recent years. The necessary condition for using computer vision to judge defects is that a certain amount of data is required to maintain a certain level of accuracy. However, defect data is more difficult to collect than good data, so the use of data augmentation methods is indispensable. The current common augmentation methods (such as shifting, flipping, rotating, etc.) may produce images that do not conform to the real situation, and the defect patterns are close. This method has a limited increase in the more diverse feature augmentation patterns. Therefore, this study proposes a data augmentation method for generating diverse defect data, which converts a limited number of real images into new defect data to achieve the purpose of making up for the number of defective data. In addition, in order to make the generated results under artificial control, the semantic segmentation of the labeled images is used in the generation system of this study to provide domain knowledge. However, the traditional semantic segmentation generation model is limited by a small amount of datasets, and the results are not ideal. Therefore, this study is inspired by the literature, proposes to generate a better-quality augmented image by splitting and stitching for defects.
顯示於類別:	[資訊工程研究所] 博碩士論文

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