پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 698 |
| تعداد مقالات | 10,094 |
| تعداد مشاهده مقاله | 71,270,473 |
| تعداد دریافت فایل اصل مقاله | 62,993,758 |
Transfer learning based deep convolutional neural network model for pavement crack detection from images | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 102، دوره 13، شماره 1، خرداد 2022، صفحه 1209-1223 اصل مقاله (1.39 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.24521.2762 | ||
| نویسندگان | ||
| S. Jana* 1؛ S. Thangam2؛ Anem Kishore1؛ Venkata Sai Kumar1؛ Saddapalli Vandana1 | ||
| 1Department of Electronics and Communication Engineering, Veltech Rangarajan Dr Sagunthala R &D Institute of Science and Technology, Chennai. Tamil Nadu-600062 | ||
| 2Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru campus, Amrita Vishwa Vidyapeetham, India | ||
| تاریخ دریافت: 22 خرداد 1400، تاریخ بازنگری: 09 تیر 1400، تاریخ پذیرش: 27 مهر 1400 | ||
| چکیده | ||
| The road is a path that supports to connect different places. It plays a crucial role in our day-today life. Improper maintenance, overloading, climate conditions, and some other elements create distress on the roads. The common distresses are Potholes, cracking, and rutting. Manually detecting the distresses means human inspection is a messy and long time-consuming process. In recent past accidents on road is on the increase due to improper maintenance of road. Efficient methods of detecting pavement damages using image processing, machine learning and deep learning techniques have been a trending research topic. Image processing algorithms mainly include edge detection, region growing methods, and threshold segmentation operations for processing the pavement images and extracting crack information from the images. Machine learning methods of pavement crack detection adapts neural networks, supervised and unsupervised learning algorithms with pavement crack image as input. With Deep learning techniques, it has been possible to detect pavement cracks with greater accuracy. In this paper, we review the deep learning methods of pavement crack detection and propose a novel method to detect pavement cracks using Deep Learning with transfer learning. We also analyzed the performance of the proposed model for different network architectures namely, Google net, Alexnet and Resent and inferred that Google net gives better performance in detecting pavement cracks. | ||
| کلیدواژهها | ||
| Pavement crack؛ Deep Learning؛ Machine Learning؛ crack detection | ||
| مراجع | ||
|
[1] L. Attard, C. J. Debono, G. Valentino, Automatic crack detection using mask R-CNN, 11th Int. Symp. Image Signal Process. Anal. (ISPA), 2019, pp. 152-157,https://doi.org/10.1109/ISPA.2019.8868619. [2] S. Bang, S. Park, H. Kim, Y. S. Yoon and H. Kim, A deep residual network with transfer learning for pixel level road crack detection. Proc. 35th ISARC, Berlin, Germany,2018, pp.753-756,https://doi.org/10.22260/ ISARC2018/0103. [3] S. Bhat, S. Naik, M. Gaonkar, P. Sawant, S. Aswale and P. Shetgaonkar, A survey on road crack detection techniques, Int. Conf. Emerging Trends Inf. Technol. Eng. (ic-ETITE), 2020, pp. 1-6, https://doi.org/10. 1109/ic-ETITE47903.2020.67 . [4] R. Fan, M. J. Bocus, Y. Zhu, J. Jiao, L. Wang, F. Ma, S. Cheng, M. Liu, Road crack detection using deep convolutional neural network and adaptive thresholding. IEEE Intell. Veh. Symp. (IV), 2019, pp. 474-479, https: //doi.org/10.1109/IVS.2019.8814000. [5] K. Gopalakrishnan, S. K. Khaitan, A. Choudhary and A. Agrawal, Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection, Constr. Build. Mater. , 157 (2017), pp. 322-330, https://doi.org/10.1016/j.conbuildmat.2017.09.110. [6] J. HUYAN, Development of machine learning-based analytical tools for pavement performance assessment and crack detection. UWSpace, (2019), http://hdl.handle.net/10012/14905. [7] M. S. Kaseko and S. G. Ritchie, A neural network-based methodology for pavement crack detection and classification, Transp. Res. Part C Emerging Technol., 1(4) (1993) 275–291, https://doi.org/10.1016/0968-090X(93) 90002-W. [8] V. Mandal, L. Uong and Y. Adu-Gyamfi, Automated road crack detection using deep convolutional neural networks, IEEE Int. Conf. Big Data(ICBD), 2018, pp. 5212-5215, https://doi.org/10.1109/BigData.2018. 8622327. [9] M. Nie and K. Wang, (2018). Pavement distress detection based on transfer learning, 5th Int. Conf. Syst. Inf. (ICSAI), 2018, pp. 435-439, https://doi.org/10.1109/ICSAI.2018.8599473. [10] N. Sharma, R. Dhir and R. Rani, Crack detection in concrete using transfer learning, Adv. Math. Sci. J., 9(6) (2020) 3895–3906, https://doi.org/10.37418/amsj.9.6.69. [11] Y. Shi, L. Cui, Z. Qi, F. Meng and Z. Chen., Automatic road crack detection using random structured forests. IEEE Trans. Intell. Transp. Syst., 17(12) (2016) 3434-3445, https://doi.org/10.1109/TITS.2016.2552248. [12] P. Subirats, J. Dumoulin, V. Legeay and D. Barba, Automation of pavement surface crack detection using the continuous wavelet transform, Int. Conf. Image Process., Atlanta, GA, 2006, pp. 3037-3040, https://doi.org/ 10.1109/ICIP.2006.313007. [13] S. Wang, F. Yang, Y. Cheng, Y. Yang and Y. Wang, Adaboost-based crack detection method for pavement, IOP Conf. Ser. Earth Environ. Sci., 189 (2018) 022005 ,https://doi.org/10.1088/1755-1315/189/2/022005. [14] L. Zhang, F.Yang, Y. D. Zhang and Y. J. Road crack detection using deep convolutional neural network, IEEE Int. Conf. Image Process. (ICIP), Phoenix, AZ, 2016, pp. 3708-3712,https://doi.org/10.1109/ICIP. 2016.7533052. | ||
|
آمار تعداد مشاهده مقاله: 17,794 تعداد دریافت فایل اصل مقاله: 10,865 |
||