论文标题
光纤CNN：扩展面膜R-CNN以改善基于图像的纤维分析
FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis
论文作者
论文摘要
纤维形材料（例如碳纳米管）具有很大的意义，因为它们的独特特性以及它们可以施加的健康风险。不幸的是，基于图像的纤维分析仍然涉及手动注释，这是一个耗时且昂贵的过程。因此，我们建议使用基于区域的卷积神经网络（R-CNN）来自动执行此任务。 Mask R-CNN是用于语义分割任务的最广泛使用的R-CNN，在分析纤维形对象时很容易出现错误。因此，引入和验证了一种新的体系结构-Fiber -CNN。 Fiber-CNN结合了两个已建立的R-CNN架构（Mask和KePoint R-CNN），并为纤维宽度和长度的预测添加了其他网络头。结果，在新型的纤维图像测试数据集上，光纤CNN能够超过面膜R-CNN的平均平均精度33％（11个百分点）。
Fiber-shaped materials (e.g. carbon nano tubes) are of great relevance, due to their unique properties but also the health risk they can impose. Unfortunately, image-based analysis of fibers still involves manual annotation, which is a time-consuming and costly process. We therefore propose the use of region-based convolutional neural networks (R-CNNs) to automate this task. Mask R-CNN, the most widely used R-CNN for semantic segmentation tasks, is prone to errors when it comes to the analysis of fiber-shaped objects. Hence, a new architecture - FibeR-CNN - is introduced and validated. FibeR-CNN combines two established R-CNN architectures (Mask and Keypoint R-CNN) and adds additional network heads for the prediction of fiber widths and lengths. As a result, FibeR-CNN is able to surpass the mean average precision of Mask R-CNN by 33 % (11 percentage points) on a novel test data set of fiber images.
