A real-time optical fiber defect detection system based on machine vision is designed to solve the problem that manual method is inefficient and imprecise in detecting fast moving fiber. Fiber defects are defined to establish a database and criterions for classification. Continuous fiber images captured automatically by 3 industrial cameras are transmitted to IPC for classification
and the IPC obtains morphological characteristics data of defects in fiber target after image processing. An advanced AdaBoost cascade classifier based on morphological characteristics is designed to analyze the defect image features. The advanced AdaBoost method and the classical AdaBoost method are tested under industry condition
and detection results show that the detection accuracy of the system with advanced AdaBoost is over 99% but the system with advanced AdaBoost takes much less time. It can be concluded that the proposed detection method has good real-time performance and a high detecting accuracy.
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