Pothole detection in bituminous road using CNN with transfer learning

被引：0

作者：

Vinodhini K.A. ^{[1
,2
]}

Sidhaarth K.R.A. ^{[1
,2
]}

机构：

[1] Department of Civil Engineering, India

[2] Vel Tech Rangarajan Dr. Sakunthala R&D Institute of Science and Technology, India

来源：

Measurement: Sensors | 2024年 / 31卷

关键词：

Bituminous road; convolutional neural network; Pothole detection; Transfer learning;

D O I：

10.1016/j.measen.2023.100940

中图分类号：

学科分类号：

摘要：

Road surfaces are highly affected by climatic changes which caused potholes and cracks. Maintenance of the road is a need-of-the-hour process for preventing the physical damage caused for vehicles. The important process in road maintenance is the detection of potholes and cracks. Automatic detection of potholes in bituminous roads is a tedious task. This paper proposed the detection of potholes using transfer learning and convolution neural networks. The results are promising, and The suggested method can provide valuable information that can be used for various ITS services. One such service is alerting drivers about potholes, allowing them to be more cautious while driving. Additionally, this information can be utilized to assess the initial maintenance needs of a road management system and promptly address any repairs or maintenance required. The achieved results through the proposed method are compared with the state-of-the-art detection algorithms like Transfer Learning + Recurrent neural network, Transfer Learning + Generated adversarial network. In that, the result obtained through the proposed method (Transfer Learning + Convolutional neural networks achieves 96 % of accuracy. © 2023 The Authors

引用

共 19 条

[1]

Joshi D., Singh T.P., Sharma G., Automatic surface crack detection using segmentation-based deep-learning approach, Eng. Fract. Mech., 268, (2022)

[2]

Vinodhini K.A., Sidhaarth K.R.A., Automatic crack detection in the pavement with lion optimization algorithm using deep learning techniques, Int. J. Adv. Des. Manuf. Technol., pp. 1-10, (2022)

[3]

Viswanathan R., Ramesh S., Subburam V., Measurement and optimization of performance characteristics in turning of Mg alloy under dry and MQL conditions, Measurement, 120, pp. 107-113, (2018)

[4]

Costin A., Adibfar A., Hu H., Chen S.S., Building Information Modeling (BIM) for transportation infrastructure–Literature review, applications, challenges, and recommendations, Autom. ConStruct., 94, pp. 257-281, (2018)

[5]

Mei Q., Gul M., Azim M.R., Densely connected deep neural network considering connectivity of pixels for automatic crack detection, Autom. ConStruct., 110, (2020)

[6]

Wang H.F., Zhai L., Huang H., Guan L.M., Mu K.N., Wang G.P., Measurement for cracks at the bottom of bridges based on tethered creeping unmanned aerial vehicle, Autom. ConStruct., 119, (2020)

[7]

Ai D., Jiang G., Lam S.K., He P., Li C., Automatic pixel-wise detection of evolving cracks on rock surface in video data, Autom. ConStruct., 119, (2020)

[8]

Dais D., Bal I.E., Smyrou E., Sarhosis V., Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning, Autom. ConStruct., 125, (2021)

[9]

Majidifard H., Adu-Gyamfi Y., Buttlar W.G., Deep machine learning approach to develop a new asphalt pavement condition index, Construct. Build. Mater., 247, (2020)

[10]

Xu Z., Guan H., Kang J., Lei X., Ma L., Yu Y., Chen Y., Li J., Pavement crack detection from CCD images with a locally enhanced transformer network, Int. J. Appl. Earth Obs. Geoinf., 110, (2022)

← 1 2 →