A U-net semantic segmentation model trained on MgAl2O4 fracture surfaces quantifies intergranular and transgranular modes in SEM images, with reported mean IoU of 91.1% on the training material and 94% on untrained Al2O3.
The VIA Annotation Software for Images, Audio and Video
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abstract
In this paper, we introduce a simple and standalone manual annotation tool for images, audio and video: the VGG Image Annotator (VIA). This is a light weight, standalone and offline software package that does not require any installation or setup and runs solely in a web browser. The VIA software allows human annotators to define and describe spatial regions in images or video frames, and temporal segments in audio or video. These manual annotations can be exported to plain text data formats such as JSON and CSV and therefore are amenable to further processing by other software tools. VIA also supports collaborative annotation of a large dataset by a group of human annotators. The BSD open source license of this software allows it to be used in any academic project or commercial application.
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Toward quantitative fractography using convolutional neural networks
A U-net semantic segmentation model trained on MgAl2O4 fracture surfaces quantifies intergranular and transgranular modes in SEM images, with reported mean IoU of 91.1% on the training material and 94% on untrained Al2O3.