Zhou, Yang and Ali, Raza and Mokhtar, Norrima and Harun, Sulaiman Wadi and Iwahashi, Masahiro (2024) MixSegNet: A Novel Crack Segmentation Network Combining CNN and Transformer. IEEE Access, 12. pp. 111535-111545. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3438112.
Full text not available from this repository.Abstract
In the domain of road inspection and structural health monitoring, precise crack identification and segmentation are essential for structural safety and disaster prediction. Traditional image processing technologies encounter difficulties in detecting cracks due to their morphological diversity and complex background noise. This results in low detection accuracy and poor generalization. To overcome these challenges, this paper introduces MixSegNet, a novel deep learning model that enhances crack recognition and segmentation by integrating multi-scale features and deep feature learning. MixSegNet integrates convolutional neural networks (CNNs) and transformer architectures to enhance the detection of small cracks through the extraction and fusion of fine-grained features. Comparative evaluations against mainstream models, including LRASPP, U-Net, Deeplabv3, Swin-UNet, AttuNet, and FCN, demonstrate that MixSegNet achieves superior performance on open-source datasets. Specifically, the model achieved a precision of 95.2%, a recall of 88.2%, an F1 score of 91.5%, and a mean intersection over union (mIoU) of 84.8%, thereby demonstrating its effectiveness and reliability for crack segmentation tasks.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI) (24K02975) |
| Uncontrolled Keywords: | Crack segmentation network; crack images; convolutional neural network; transformer model; image processing; deep learning; self-attention mechanism; Crack segmentation network; crack images; convolutional neural network; transformer model; image processing; deep learning; self-attention mechanism |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 09 Dec 2024 05:06 |
| Last Modified: | 09 Dec 2024 05:06 |
| URI: | http://eprints.um.edu.my/id/eprint/47134 |
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