應用機器視覺於玻璃瓶瑕疵檢測

玻璃為一種透明、不透氣且具一定硬度的物料，主要成分為二氧化矽（SiO2，即石英，砂的主要成分）。因其化學性質穩定與價格低廉，使得玻璃瓶於日常生活環境中使用量相當龐大，然而玻璃瓶的製造過程中容易產生破泡、雜質、裂痕、質變等缺陷，造成原物料的浪費、成本提升。傳統檢測方法為僱用「瓶」檢員，對玻璃瓶進行生產線上逐一檢測，但玻璃瓶的生產量數以百萬計，檢測過程枯燥，由於檢測時需面對強光，使目檢員無法持續且快速的判斷瑕疵，故易導致判斷準確率隨著時間降低，而產生漏檢之情形。機器視覺 (Machine Vision) 技術為機器模仿人類視覺的光學系統，其結合工業相機、鏡頭、電腦等工業元件，可有效擷取、分析及解釋所得之檢測影像內容，並可進行線上即時全面檢測，提供高精準度以及不間斷之工作型態。台灣為玻璃瓶之主要生產國之一，對於玻璃瓶自動化檢測之需求急迫，但目前國內仍無玻璃瓶檢測系統之相關研究，故本研究目的為運用機器視覺建構一高精準度、高效率且穩定性高之自動化玻璃瓶瑕疵檢測系統。本研究系統建構方式分為硬體與軟體兩方面進行；硬體部分，利用鋁擠型、工業電腦、光源、工業相機及相關機構進行系統硬體建構；系統軟體部份為結合 Euresys eVision與Borland C++ Builder為主體進行建構。系統效能經測試，其平均整體正確率達99.68%。本研究另外進行相關影像分割演算法，分水嶺演算法、均值飄移演算法與連通元件標記法之比較，並擇優置入系統，經測試連通元件標記法較為適合本系統。

關鍵字

電腦視覺檢測玻璃瓶；影像處理；瑕疵檢測

並列摘要

Glass bottle is one of the most important utensils for human being. Various kinds of bottle products are used in large amount in food and drink production. Since bottles may have some defects that may cause negative impact, even dangerous consequences, for production and customers, empty glass bottles requires a 100% on-line inspection after production. Currently, this kind of work is most likely performed by well-trained human inspector. Manual inspection not only increases labor cost but is very difficult to guarantee inspection quality. Therefore, machine vision-based empty bottle inspection becomes a crucial issue for glass bottle industry. The area of empty bottle inspection requires more DIP (Digital Image Processing) and lighting knowledge, and experimental study due to the transparency feature of glass bottles. Therefore, the objective of this study is to develop an empty bottle inspection system which adopts machine vision to effectively extract and identify the bottle defects. This study is planned to develop two major parts: glass bottle lighting device module and DIP bottle defect detection algorithm. All the defective bottle examples will be collected by H Com., and then DIP inspection algorithm and lighting device module will be developed in this study.

並列關鍵字

computer vision inspection ； glass bottles ； image processing ； defect detection

參考文獻

[6] Wei-Chen Li, and Du-Ming Tsai, Defect Inspection in Low-contrast LCD Images Using Hough Transform-based Non-stationary Line Detection, Master, Department of Industrial Engineering and Management Yuan-Ze University, Taiwan, R.O.C. ,2009

[7] Du-Ming Tsai, and Hsin-Yang Tsai, "Low-contrast surface inspection of mura defects in liquid crystal displays using optical flow-based motion analysis," Machine Vision and Applications, 2011, pp. 629-649

[9] Jun Wang, and Anand K. Asundi, "A computer vision system for wineglass defect inspection via Gabor-filter-based texture features," Information Sciences 127, 2000, pp. 157-171

[11] Feng Duan, Yao-Nan Wang, Huan-Jun Liu, and Yang-Guo Li, "A Real-time Machine Vision System for Bottle Finish Inspection," 8th International Conference on Control, Automation, Robotics and Vision Kunming, 2004

[12] Feng Duan, Yao-Nan Wang, Huan-Jun Liu, and Yang-Guo Li, "Glass Bottle Inspector Based on Machine Vision," World Academy of Science, Engineering and Technology 20, 2008

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應用機器視覺於玻璃瓶瑕疵檢測

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