In this paper, a set of improvements made in drill wear recognition algorithm obtained during previous work is presented. Images of the drilled holes made on melamine faced particleboard were used as its input values. During the presented experiments, three classes were recognized: green, yellow and red, which directly correspond to a tool that is in good shape, shape that needs to be confirmed by an operator, and which should be immediately replaced, since its further use in production process can result in losses due to low product quality. During the experiments, and as a direct result of a dialog with a manufacturer it was noted that while overall accuracy is important, it is far more crucial that the used algorithm can properly distinguish red and green classes and make no (or as little as possible) misclassifications between them. The proposed algorithm is based on an ensemble of possibly diverse models, which performed best under the above conditions. The model has relatively high overall accuracy, with close to none misclassifications between indicated classes. Final classification accuracy reached 80.49% for biggest used window, while making only 7 critical errors (misclassifications between red and green classes).

中文翻译：

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基于三聚氰胺刨花板钻孔图像的钻孔磨损分类深度学习方法

在本文中，提出了在先前工作中获得的钻头磨损识别算法的一系列改进。在三聚氰胺饰面刨花板上钻孔的图像用作其输入值。在展示的实验中，识别出三类：绿色、黄色和红色，它们直接对应于形状良好的工具，需要操作员确认的形状，并且应立即更换，因为它进一步用于生产过程中可能会因产品质量低下而造成损失。在实验过程中，作为与制造商对话的直接结果，有人指出，虽然整体准确性很重要，但更重要的是所使用的算法可以正确区分红色和绿色类别，并避免（或尽可能少） ) 他们之间的错误分类。所提出的算法基于一组可能不同的模型，这些模型在上述条件下表现最佳。该模型具有相对较高的整体准确度，在指定类别之间几乎没有错误分类。最大使用窗口的最终分类准确率达到了 80.49%，而只有 7 个严重错误（红色和绿色类别之间的错误分类）。