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DIGITAL QUALITY CONTROL USING COMPUTER VISIONING WITH DEEP LEARNING

机译:计算机视觉与深度学习相结合的数字质量控制

摘要

Implementations include receiving sample data, the sample data being generated as digital data representative of a sample of the product, providing a set of features by processing the sample data through multiple layers of a residual network, a first layer of the residual network identifying one or more features of the sample data, and a second layer of the residual network receiving the one or more features of the first layer, and identifying one or more additional features, processing the set of features using a CNN to identify a set of regions, and at least one object in a region of the set of regions, and determine a type of the at least one object, and selectively issuing an alert at least partially based on the type of the at least one object, the alert indicating contamination within the sample of the product.
机译:实现方式包括:接收样本数据,样本数据作为代表产品样本的数字数据生成,通过通过残差网络的多层处理样本数据来提供一组功能,残差网络的第一层标识一个或多个。样本数据的更多特征,以及剩余网络的第二层接收第一层的一个或多个特征,并标识一个或多个其他特征,使用CNN处理该组特征以标识一组区域,以及在该组区域中的一个区域中的至少一个物体,并确定至少一个物体的类型,并至少部分地基于至少一个物体的类型选择性地发出警报,该警报指示样品内的污染产品的

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