PCTC-Net: A Crack Segmentation Network with Parallel Dual Encoder Network Fusing Pre-Conv-Based Transformers and Convolutional Neural Networks

Cracks are common defects that occur on the surfaces of objects and structures. Crack detection is a critical maintenance task that traditionally requires manual labor. Large-scale manual inspections are expensive. Research has been conducted to replace expensive human labor with cheaper computing r...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-02, Vol.24 (5), p.1467
Main Authors: Moon, Ji-Hwan, Choi, Gyuho, Kim, Yu-Hwan, Kim, Won-Yeol
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Bias
CNN
Computational linguistics
crack
Cracks
Datasets
Deep learning
Electric transformers
Language processing
Medical imaging equipment
Natural language interfaces
Neural networks
PCTC-Net
Pre-Conv
segmentation
transformer
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Summary:Cracks are common defects that occur on the surfaces of objects and structures. Crack detection is a critical maintenance task that traditionally requires manual labor. Large-scale manual inspections are expensive. Research has been conducted to replace expensive human labor with cheaper computing resources. Recently, crack segmentation based on convolutional neural networks (CNNs) and transformers has been actively investigated for local and global information. However, the transformer is data-intensive owing to its weak inductive bias. Existing labeled datasets for crack segmentation are relatively small. Additionally, a limited amount of fine-grained crack data is available. To address this data-intensive problem, we propose a parallel dual encoder network fusing Pre-Conv-based Transformers and convolutional neural networks (PCTC-Net). The Pre-Conv module automatically optimizes each color channel with a small spatial kernel before the input of the transformer. The proposed model, PCTC-Net, was tested with the DeepCrack, Crack500, and Crackseg9k datasets. The experimental results showed that our model achieved higher generalization performance, stability, and F1 scores than the SOTA model DTrC-Net.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24051467