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PCTC-Net: A Crack Segmentation Network with Parallel Dual Encoder Network Fusing Pre-Conv-Based Transformers and Convolutional Neural Networks.
Moon, Ji-Hwan; Choi, Gyuho; Kim, Yu-Hwan; Kim, Won-Yeol.
Afiliación
  • Moon JH; Department of Artificial Intelligence Engineering, Chosun University, Gwangju 61452, Republic of Korea.
  • Choi G; Department of Artificial Intelligence Engineering, Chosun University, Gwangju 61452, Republic of Korea.
  • Kim YH; Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.
  • Kim WY; Department of Artificial Intelligence Engineering, Chosun University, Gwangju 61452, Republic of Korea.
Sensors (Basel) ; 24(5)2024 Feb 24.
Article en En | MEDLINE | ID: mdl-38475003
ABSTRACT
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.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article