数据增强与损失函数在钻头磨损检测语义图像分割中的评估
Evaluation of Data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear Detection
AI总结：
本文评估了基于U-Net的语义分割在钻头磨损检测中的性能，比较了二分类与多分类、多种损失函数及数据增强策略，发现中等增强和IoU损失的二分类模型效果最佳。
摘要：
刀具磨损监测对于制造过程中的质量控制与成本降低至关重要，钻削应用便是其中的一个例子。本文提出了一种基于U-Net的语义图像分割流程，应用于切削刀片的显微图像，以实现磨损检测。磨损区域被区分为两种不同类型，从而构成一个多类分类问题。另一方面，将两种磨损类型合并为一个通用磨损类别，则可将问题表述为二分类任务。除了对二分类与多分类问题进行比较外，还研究了不同的损失函数，即交叉熵、焦点交叉熵以及基于交并比（IoU）的损失函数。此外，模型在不同尺寸的图像块上进行训练，并采用了不同强度的增强技术。我们发现，性能最佳的模型是二分类模型，其训练数据采用中等程度的增强，并使用基于IoU的损失函数。
英文摘要：
Tool wear monitoring is crucial for quality control and cost reduction in manufacturing processes, of which drilling applications are one example. In this paper, we present a U-Net based semantic image segmentation pipeline, deployed on microscopy images of cutting inserts, for the purpose of wear detection. The wear area is differentiated in two different types, resulting in a multiclass classification problem. Joining the two wear types in one general wear class, on the other hand, allows the problem to be formulated as a binary classification task. Apart from the comparison of the binary and multiclass problem, also different loss functions, i. e., Cross Entropy, Focal Cross Entropy, and a loss based on the Intersection over Union (IoU), are investigated. Furthermore, models are trained on image tiles of different sizes, and augmentation techniques of varying intensities are deployed. We find, that the best performing models are binary models, trained on data with moderate augmentation and an IoU-based loss function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
