本篇論文提出一套針對小鋼胚表面缺陷的自動偵測與分析系統。此系統分析現場即時拍攝的小鋼胚影像是否有缺陷，並進一步分析缺陷種類。系統主要包含了三個模組:影像處理與缺陷定位模組、缺陷特徵篩選模組，以及缺陷分類模組。為了配合線上即時處理，在影像處理與缺陷定位模組中我們採用複雜度較低的演算法，且在演算法中加入加速的機制。在缺陷分類之前，需要擷取分類所需的特徵，而擷取的特徵必頇具有區分缺陷類別的能力。因此我們設計了一個特徵篩選的模組，可以針對各種分類問題，從候選特徵中篩選出具有辨識能力的特徵組合。此模組是利用禁搜尋法(Tabu Search)配合KNN分類器找出最佳的特徵組合。在現場應用上缺陷樣本會隨著時間增加，因此需要大量的空間儲存缺陷樣本，也需要大量的時間重新訓練分類器，於是我們的分類模組採用了Learn++增量學習分類器以解決上述問題。 實驗過程中，我們請現場專家用人眼判定影像的缺陷所在，接著與我們的偵測結果做比較。實驗結果顯示我們設計的模組能快速並準確的偵測出缺陷所在位置，並可以準確的判別缺陷種類。且在分類模組中，使用增量學習法訓練分類器比起傳統倒傳遞類神經網路法(BPN)節省了大量的時間。

In this thesis we present an automatic defect detection and analysis system for steel billet. In practice, the system analyzes the sequentially acquired steel billet images to locate defects on the steel surface and recognizes the types of detected defects. The proposed system is consisted of three modules: (1) Image processing and defect detection, (2) Feature extraction and selection, (3) Incremental learning classifier. For the purpose of on-line detection, we use low complexity algorithms in the first module. We also adopt some strategies to speed up the computation. Before classification, it is important to find out the features that have better ability to distinguish defect classes. Therefore, we design a feature selection module which combines Tabu Search with k-nearest neighbor classifier to obtain the best set of features for classification. In practice, defect samples will ceaselessly arrive and thus increase the cost of training time to update classifier and more memory space to store these samples. In this study, we utilize the Learn++ classifier to overcome the problem mentioned above.
In the experiment, some expert detection results were used to judge the correctness of the proposed defect detection. The results show that the proposed system not only detects defect correctly and rapidly, but also correctly recognizes classes of steel defect. In the classification module, training the classifier with incremental learning algorithm saves a lot of time than that with the conventional BPN skill.

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