ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple Noises and Various Morphologies of Cracks
Cracks are a common early road defect that tends to worsen with the aging of roads, potentially leading to severe structural damage. Timely and accurate crack detection plays a crucial role in mitigating such risks and holds significant importance for infrastructure maintenance. Deep learning techni...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/6/971 |
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| Summary: | Cracks are a common early road defect that tends to worsen with the aging of roads, potentially leading to severe structural damage. Timely and accurate crack detection plays a crucial role in mitigating such risks and holds significant importance for infrastructure maintenance. Deep learning techniques have demonstrated excellent performance in image-based crack extraction tasks. However, challenges persist due to the presence of numerous noisy pixels in the image background and the diverse and intricate morphologies of cracks, leading to issues such as misclassification and omission. To address these issues, this paper proposes a refined pixel-level segmentation network (ANF-Net) suitable for complex crack detection scenarios with high noise levels and diverse crack morphologies. When extracting crack features, on one hand, the network introduces an attention module tailored for crack scenes to learn pixel-wise feature weights, enabling the network to focus on crack regions and thereby reducing the impact of similar background features, mitigating false positives caused by noise misclassification. On the other hand, a constrained multi-morphological convolution structure is constructed by imposing learnable continuous constraints on the deformation offsets of convolutional kernels, allowing the network to adaptively fit different crack shapes. This design enhances the network’s ability to extract cracks in morphologically diverse, narrow, and densely populated regions, effectively preventing issues such as crack extraction interruptions and omissions. Additionally, a multi-scale discrete wavelet transform enhancement module is designed to assist the network in considering frequency domain information that contains crack features, further improving its feature extraction capability. Simulations are conducted using three publicly available crack datasets, and the proposed method is compared with mainstream segmentation models. The results demonstrate that the proposed method achieves F1 scores of 87.9%, 82.5%, and 71.5% on the three datasets, respectively, all of which surpass the performance of current mainstream segmentation models. The proposed network accurately extracts road cracks and exhibits robust performance. |
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| ISSN: | 2072-4292 |