This paper presents a modification to be added to multiclass classifiers, that improves their performance when classifying, in this case, defects appearing in polyethylene films. It aims to classify a new defect by confronting every defect type against each of the other types. In a simplified way, the type that results winner in more matches is the type that the defect belongs to. Different ways of implementing neural networks have been tested, using Gradient Descent and techniques for backpropagation. These techniques have been formally and understandably explained. In addition, a method based on decision trees has been included for comparison. Different issues related to the practical implementation of the detection and identification system within an installed production chain are addressed. The resulting system has been incorporated as a real inspection automatism in a polyethylene manufacturing line, and trained with defects previously obtained from the same line.

本文提出了一种要添加到多类分类器中的改进，当对聚乙烯薄膜中出现的缺陷进行分类时，可以改进它们的性能。它的目的是通过将每种缺陷类型与其他每种类型进行对抗来对新缺陷进行分类。以简化的方式，导致更多匹配的获胜者的类型是缺陷所属的类型。使用梯度下降和反向传播技术，已经测试了实现神经网络的不同方法。这些技术已被正式且可理解地解释。另外，还包括了一种基于决策树的方法进行比较。解决了与已安装的生产链中的检测和识别系统的实际实施相关的不同问题。