The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
A cost effective solution for pavement crack inspection using cameras and deep neural networks
Abstract
Abstract Automatic crack detection on pavement surfaces is an important research field in the scope of developing an intelligent transportation infrastructure system. In this paper, a cost effective solution for road crack inspection by mounting the commercial grade sport camera, GoPro, on the rear of the moving vehicle is introduced. Also, a novel method called ConnCrack combining conditional Wasserstein generative adversarial network and connectivity maps is proposed for road crack detection. In this method, a 121-layer densely connected neural network with deconvolution layers for multi-level feature fusion is used as generator, and a 5-layer fully convolutional network is used as discriminator. To overcome the scattered output issue related to deconvolution layers, connectivity maps are introduced to represent the crack information within the proposed ConnCrack. The proposed method is tested on a publicly available dataset as well our collected data. The results show that the proposed method achieves state-of-the-art performance compared with other existing methods in terms of precision, recall and F1 score.
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Cited by
Kheradmandi, N., & Mehranfar, V. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.126162 · Cited by 426
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
Hamishebahar, Y., Guan, H., So, S., & Jo, J. (2022) · Applied Sciences · doi:10.3390/app12031374 · Cited by 188
Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review
Mishra, M., & Lourenço, P. B. (2024) · Journal of Cultural Heritage · doi:10.1016/j.culher.2024.01.005 · Cited by 165
Taheri, A., & Sobanjo, J. (2024) · Infrastructures · doi:10.3390/infrastructures9060090 · Cited by 17
Sim-to-Real Transfer for Object Detection in Aerial Inspections of Transmission Towers
Peterlevitz, A. J., Chinelatto, M. A., Menezes, A. G., Motta, C. A. M., Pereira, G. A. B., Lopes, G. L., Souza, G. D. M., Rodrigues, J., Godoy, L. C., Koller, M. A. F. F., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3322374 · Cited by 7
Metadata
- Authors
- Mei, Q., & Gül, M.
- Venue
- Construction and Building Materials
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 256
- Pages
- 119397
- ISSN
- 0950-0618 (ISSN-L)
- OpenAlex
- W3021470593
- Cited by
- 253
- References
- 62
- FWCI
- 19.66
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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