結合機器視覺與深度學習之金屬圓柱表面缺陷檢測系統

Machine Vision and Deep Learning Based Defect Inspection System for Cylindrical Metallic Surface

自2012年加拿大Alex Krizhevsky所領軍的團隊贏得ImageNet的Large Scale Visual Recognition Competition冠軍開始，深度學習領域掀起了新一波人工智慧的浪潮，相關的產業與研究團隊都競相投入此深度學習的研究領域。本論文著墨於探討深度學習是否適用於自動光學檢測（AOI）領域與產業、分析光學檢測的問題是否可直接套用現行的深度學習模型、及分析相較於語音識別領域、自然語言處理領域、醫療或其它電腦視覺領域，應用深度學習方法於光學瑕疵檢測的研究論文仍相對欠缺不足的原因。本文並舉金屬圓柱表面（如高爾夫球桿）的瑕疵檢測應用為實例，來探討深度學習應用於自動光學檢測瑕疵之方法，以及當結合機器視覺與深度學習技術時，可能遭遇的問題與克服方案。 Since the Alex Krizhevsky team won the championship of ImageNet Large Scale Visual Recognition Competition in 2012, deep learning has set off a new wave of artificial intelligence field, with all industries and research groups competing for deep learning field. However, the open question is whether the deep learning is the magic solution in a fairy tale or not. In this paper, we discuss how to employ the deep learning in the field of automated optical inspection industry, and analyze the skills of directly applying the current deep learning model to the defect detection based on the optical inspection method. Compared to the deep learning in the fields of speech recognition, natural language processing, medical or other computer vision applications, few literatures are found in discussing the defect detection based on deep learning. Therefore, this paper offered the solution of applying the deep learning in the field of defect detection for golf clubs based on the optical inspection method.