Open AccessPromising crack segmentation method based on gated skip connection
Author(s) -
Jabreel M.,
AbdelNasser M.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.3919
Subject(s) - encoder , benchmark (surveying) , connection (principal bundle) , segmentation , computer science , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , structural engineering , engineering , geology , linguistics , philosophy , geodesy , operating system
This Letter proposes a promising deep learning‐based method for crack segmentation based on gated skip connection. The proposed gated skip connection enables the decoder layers to promote crack‐aware feature representations from the encoder layers by applying high weights on the crack‐relevant features that come from the encoder layers and lower weights for irrelevant features. Unlike the related methods, the authors do not apply any pre‐processing or refinement steps to improve the crack segmentation results. The proposed method beats the state‐of‐the‐art methods with an open benchmark database (IoU of 87.5).