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Small-sample data-driven lightweight convolutional neural network for asphalt pavement defect identification
- Source :
- Case Studies in Construction Materials, Vol 21, Iss , Pp e03643- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- Automatic detection technology provides a reliable method for civil engineering distress detection. However, to overcome limitations of computational resources and the significant cost of image acquisition, this study proposes a simplified network parameter-based pavement crack classification network (PCCNet) to achieve efficient and robust crack classification. Firstly, a lightweight classification model is developed based on a shuffle unit and inverted residual architecture, designed to deliver high-performance pavement crack classification with limited computing resources. Secondly, a novel training method is proposed to accurately identify pavement defects on small-sample pavement images datasets. Additionally, the interpretability of neural network in pavement defect detection is enhanced by visualizing training process. The results demonstrate that the model achieved a classification accuracy of 97.89 % on the augmented pavement image dataset and a classification accuracy of over 83 % on multi-source asphalt pavement images. Furthermore, visualizing intermediate features further enhanced the high-precision recognition ability of the lightweight model.
Details
- Language :
- English
- ISSN :
- 22145095
- Volume :
- 21
- Issue :
- e03643-
- Database :
- Directory of Open Access Journals
- Journal :
- Case Studies in Construction Materials
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7612fbe976ed4235a36856d76f057a0b
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.cscm.2024.e03643