NDLI: Efficient attention-based deep encoder and decoder for automatic crack segmentation

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Efficient attention-based deep encoder and decoder for automatic crack segmentation

Content Provider	SAGE Publishing
Author	Kang, Dong H. Cha, Young-Jin
Copyright Year	2021
Abstract	Recently, crack segmentation studies have been investigated using deep convolutional neural networks. However, significant deficiencies remain in the preparation of ground truth data, consideration of complex scenes, development of an object-specific network for crack segmentation, and use of an evaluation method, among other issues. In this paper, a novel semantic transformer representation network (STRNet) is developed for crack segmentation at the pixel level in complex scenes in a real-time manner. STRNet is composed of a squeeze and excitation attention-based encoder, a multi head attention-based decoder, coarse upsampling, a focal-Tversky loss function, and a learnable swish activation function to design the network concisely by keeping its fast-processing speed. A method for evaluating the level of complexity of image scenes was also proposed. The proposed network is trained with 1203 images with further extensive synthesis-based augmentation, and it is investigated with 545 testing images (1280 × 720, 1024 × 512); it achieves 91.7%, 92.7%, 92.2%, and 92.6% in terms of precision, recall, F1 score, and mIoU (mean intersection over union), respectively. Its performance is compared with those of recently developed advanced networks (Attention U-net, CrackSegNet, Deeplab V3+, FPHBN, and Unet++), with STRNet showing the best performance in the evaluation metrics-it achieves the fastest processing at 49.2 frames per second.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/14759217211053776?download=true
Starting Page	2190
Ending Page	2205
Page Count	16
ISSN	14759217
Issue Number	5
Volume Number	21
Journal	Structural Health Monitoring (SHM)
e-ISSN	17413168
DOI	10.1177/14759217211053776
Language	English
Publisher	Sage Publications UK
Publisher Date	2021-12-19
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2021
Subject Keyword	Image segmentation semantic segmentation damage detection real-time processing computer vision image synthesis pixel-level classification image analysis deep learning concrete crack segmentation
Content Type	Text
Resource Type	Article
Subject	Biophysics Mechanical Engineering

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