FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis

• URL: http://arxiv.org/abs/2006.04552v2
• Date: Mon, 19 Oct 2020 08:45:14 GMT
• Title: FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis
• Authors: Max Frei, Frank Einar Kruis
• Abstract summary: We propose the use of region-based convolutional neural networks (R-CNNs) to automate image-based analysis of fibers. FibeR-CNN is able to surpass the mean average precision of Mask R-CNN by 33 % on a novel test data set of fiber images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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