Abstract
Surface crack segmentation poses a challenging computer vision task as background, shape, color and size of cracks vary. In this work we propose optimized deep encoder-decoder methods consisting of a combination of techniques which yield an increase in crack segmentation performance. Specifically we propose a decoder-part for an encoder-decoder based deep learning architecture for semantic segmentation and study its components to achieve increased performance. We also examine the use of different encoder strategies and introduce a data augmentation policy to increase the amount of available training data. The performance evaluation of our method is carried out on four publicly available crack segmentation datasets. Additionally, we introduce two techniques into the field of surface crack segmentation, previously not used there: Generating results using test-time-augmentation and performing a statistical result analysis over multiple training runs. The former approach generally yields increased performance results, whereas the latter allows for more reproducible and better representability of a methods results. Using those aforementioned strategies with our proposed encoder-decoder architecture we are able to achieve new state of the art results in all datasets.
Original language | English |
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Article number | 102907 |
Journal | Digital Signal Processing |
Volume | 108 |
Early online date | 7 Nov 2020 |
DOIs | |
Publication status | Published - 31 Jan 2021 |
Keywords
- crack segmentation
- convolutional neural network
- deep learning
- semantic segmentation
- Deep learning
- Semantic segmentation
- Convolutional neural network
- Crack segmentation
ASJC Scopus subject areas
- Artificial Intelligence
- Signal Processing
- Applied Mathematics
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Statistics, Probability and Uncertainty
- Computational Theory and Mathematics