A Lightweight CNN-Based Vision System for Concrete Crack Detection on a Low-Power Embedded Microcontroller Platform

Procedia Computer Science - Tập 207 - Trang 3948-3956 - 2022
Laura Falaschetti1, Mattia Beccerica2, Giorgio Biagetti1, Paolo Crippa1, Michele Alessandrini1, Claudio Turchetti1
1DII - Department of Information Engineering, Universita Politecnica delle Marche, via Brecce Bianche, 12, I-60131 Ancona, Italy
2Universita Politecnica delle Marche, via Brecce Bianche, 12, I-60131 Ancona, Italy

Tài liệu tham khảo

Xi, 2020, Deep learning model of concrete dam deformation prediction based on CNN, IOP Conference Series: Earth and Environmental Science, 580 Shi, 2016, Automatic road crack detection using random structured forests, IEEE Transactions on Intelligent Transportation Systems, 17, 3434, 10.1109/TITS.2016.2552248 Cao, 2020, Review of pavement defect detection methods, IEEE Access, 8, 14531, 10.1109/ACCESS.2020.2966881 Falaschetti, 2021, A low-cost, low-power and realtime image detector for grape leaf esca disease based on a compressed cnn, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 11, 468, 10.1109/JETCAS.2021.3098454 L. Ali, F. Alnajjar, H. A. Jassmi, M. Gocho, W. Khan, M. A. Serhani, Performance evaluation of deep CNN-based crack detection andlocalization techniques for concrete structures, Sensors 21 (5). L. Deng, H.-H. Chu, P. Shi, W. Wang, X. Kong, Region-based CNN method with deformable modules for visually classifying concrete cracks,Applied Sciences (Switzerland) 10 (7). Choi, 2020, SDDNet: Real-time crack segmentation, IEEE Transactions on Industrial Electronics, 67, 8016, 10.1109/TIE.2019.2945265 Dorafshan, 2018, Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete, Construction and Building Materials, 186, 1031, 10.1016/j.conbuildmat.2018.08.011 Mohammed, 2021, Exploring the detection accuracy of concrete cracks using various CNN models, Advances in Materials Science and Engineering, 10.1155/2021/9923704 Hacefendiolu, 2022, Concrete road crack detection using deep learning-based faster R-CNN method, Iranian Journal of Science and Technology - Transactions of Civil Engineering, 46, 1621 1633 Ç. F. Özgenel, Concrete crack images for classification, http://dx.doi.org/10.17632/5y9wdsg2zt.2, mendeley Data, V2, Accessed: 2022-05-01 (2019). Özgenel, 2018, Performance comparison of pretrained convolutional neural networks on crack detection in buildings, 693 Zhang, 2016, Road crack detection using deep convolutional neural network, 3708 Dorafshan, 2018, SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks, Data in Brief, 21, 1664, 10.1016/j.dib.2018.11.015 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, CoRR abs/1512.03385. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: Efficient convolutional neuralnetworks for mobile vision applications, CoRR abs/1704.04861. M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, L. Chen, Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation, CoRR abs/1801.04381. R. J. Wang, X. Li, S. Ao, C. X. Ling, Pelee: A real-time object detection system on mobile devices, CoRR abs/1804.06882. F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, K. Keutzer, Squeezenet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size, CoRR abs/1602.07360. Lecun, 1998, Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 2278, 10.1109/5.726791