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About Deep Learning-Based Instance Segmentation of Cracks From Shield Tunnel Lining Images
This document describes a study that uses a deep learning method called Mask R-CNN to perform instance segmentation of cracks in images of shield tunnel linings. Mask R-CNN is a convolutional neural network that can both detect objects in an image and generate a segmentation mask for each detected object. The researchers trained Mask R-CNN models on a dataset of over 1,100 labeled crack instances from tunnel lining images. They tested different model configurations and found that incorporating a morphological closing operation into Mask R-CNN helped connect disjoint cracks belonging to the same instance, improving segmentation performance. Their best model achieved a balanced accuracy of 82% and F1 score of 69% for crack instance segmentation.
Detailed Information
| Author: | ['Eman Saleh'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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