FFEDN: Feature Fusion Encoder Decoder Network for Crack Detection

Crack detection plays a crucial role in structural health monitoring tasks to ensure the reliability of the transportation infrastructures. However, the automatic detection of cracks remains a challenging task due to the complicated background. Especially, tiny crack detection should be attached imp...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 23; no. 9; pp. 15546 - 15557
Main Authors: Liu, Chuanqi, Zhu, Chengguang, Xia, Xuan, Zhao, Jiankang, Long, Haihui
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
attention mechanism
Coders
Crack detection
Cracks
Decoding
encoder-decoder
Feature extraction
feature fusion
Flaw detection
Image edge detection
Interference
Modules
Reliability engineering
Semantics
Shape
shape semantic prior
Structural health monitoring
Task analysis
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:Crack detection plays a crucial role in structural health monitoring tasks to ensure the reliability of the transportation infrastructures. However, the automatic detection of cracks remains a challenging task due to the complicated background. Especially, tiny crack detection should be attached importance because of its weak feature and background interference. Therefore, an end-to-end network Feature Fusion Encoder Decoder Network (FFEDN) with two novel modules is proposed to improve the crack detection accuracy. For one thing, the representation capability for tiny cracks is enhanced by introducing the attention mechanism, which redistributes and fuses different features of both the encoder and the decoder. For another, because high-level feature contains less interference, a shape semantic prior module is developed to learn the shape prior map that provides the rough shape and location information of cracks. This map is fed into the lower-level feature and helps it focus on crack areas, thereby suppressing background interference. To demonstrate the effectiveness of the proposed network, several experiments are implemented on three publicly available crack datasets. Compared with state-of-the-art crack detection methods, the novel network shows better performance on all the six evaluation metrics.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3141827