目前印刷電路板(Printed Circuit Board)表面的瑕疵檢驗系統,主要是針對線路幾何瑕疵檢測與表面黏著錫點檢測這二個方向,甚少對PCB電鍍表面進行瑕疵檢測的工作,且多僅利用灰階影像資訊進行影像分析,由於灰階影像所能提供的影像資訊(gray-value)不如彩色影像資訊(R, G, B)豐富,無法像彩色影像資訊能更完整的將影像資訊特徵呈現出來,因此本研究將利用彩色機器視覺技術,針對PCB之金手指(edge connector)電鍍表面進行自動瑕疵檢測。 本研究主要目標是發展一套適合於線上即時檢測的PCB金手指表面瑕疵檢測技術。由於目前的紋路瑕疵檢測技術多採用圖樣比對 (pattern match)或紋路特徵萃取(feature extraction)這兩大方向進行檢測的工作,因圖樣比對法缺點為比對效果會受旋轉、位移與光源的影響,而特徵萃取法於轉換的計算複雜導致計算時間長,不適合於即時性的生產檢測工作,因此本研究利用彩色影像資訊,藉由色彩模型轉換後的色彩特徵值選取,與資訊理論(information theory)中用於評估資訊內涵複雜度之量化衡量指標“熵”(entropy)結合,利用熵演算法的快速計算,衡量PCB電鍍表面(金手指)紋路的規則性與一致性,將破壞紋路規則性與一致性的瑕疵凸顯出來。 本研究分別提出利用雙色彩特徵資訊、紋路之方向角度資訊與熵結合,探討上述二種熵之檢測衡量指標對於金手指表面之色彩和結構瑕疵的檢測能力,由實驗結果得知,本研究對於金手指表面瑕疵具有良好的檢測凸顯能力,在其他工業產品的應用如紡織品、紙製品與金屬切削工件表面,本研究也能將破壞紋路規則性與一致性的瑕疵檢測凸顯出來。
Various automated visual inspection systems for printed circuit boards (PCBs) have been developed in the past years. However, most of the visual inspection techniques use only gray-level information of PCB images and focus mainly on line-etched defects. In this study, we employ color machine vision to inspect defects on electroplated surfaces of PCBs and in particular, edge connectors. The electroplated surfaces of edge connectors can be considered as a homogeneous texture. Traditional texture analysis techniques such as co-occurrence matrix methods in the spatial domain, and Fourier-based features in the spectral domain are too computationally expensive to develop an efficient inspection system. In this study, we develop two entropy measures to evaluate the homogeneity of edge connector surfaces. One entropy measure uses two color features to detect color anomalies such as oxygenation, and the other uses edge angles to detect structural defects such as scratches on electroplated surfaces. Experimental results have shown that the purposed method is reliable in detection and efficient in computation. It takes only 1 second to detect 7 edge-connector pins in one image.
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