Somesh Devagekar
The quality of uni-directional tape in production process is affected by environmental conditions like temperature and production speed. Machine vision algorithms on the scanned images are deployed in this context to detect and classify tape damages during the manufacturing procedure. We perform a comparative study among famous feature descriptors for fault candidate generation, then propose own features for fault detection using various machine learning techniques. The empirical results demonstrate the high performance of the proposed system and show preference of random forest and canny edges for classifier and feature generator respectively.
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Telecommunications System & Management received 109 citations as per Google Scholar report
