Abstract
The presence of potholes on the roads is one of the major causes of road accidents as well as wear and tear of vehicles. Various methods have been implemented to solve this problem ranging from manual reporting to authorities to the use of vibration-based sensors to 3D reconstruction using laser imaging. However, these methods have some limitations such as the high setup cost, risk while detection or no provision for night vision. In this work, we use the Mask R-CNN model to detect potholes, as it provides exceptional segmentation results. We synthetically generate a dataset for potholes, annotate it, do data augmentation and perform transfer learning on top of Mask R-CNN model which is pre-trained on MS COCO dataset. This support system was tested in varying lighting and weather conditions and was performed well in these situations as well.
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References
Srivastava S, Sharma A, Balot H (2018) Analysis and improvements on current pothole detection techniques. In: 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE). IEEE, pp 1–4
Song H, Baek K, Byun Y (2018) Pothole detection using machine learning. Advanced Science and Technology, pp 151–155
Tedeschi A, Benedetto F (2017) A real-time automatic pavement crack and pothole recognition system for mobile android-based devices. Adv Eng Inform 32:11–25
Seraj F, van der Zwaag BJ, Dilo A, Luarasi T, Havinga P (2015) Roads: a road pavement monitoring system for anomaly detection using smart phones. In: Big data analytics in the social and ubiquitous context. Springer, Berlin, pp 128–146
Georgieva K, Koch C, König M (2015) Wavelet transform on multi-GPU for real-time pavement distress detection. In: Computing in civil engineering, pp 99–106
Koch C, Brilakis I (2011) Pothole detection in asphalt pavement images. Adv Eng Inform 25(3):507–515
Thekkethala MV, Reshma S et al (2016) Pothole detection and volume estimation using stereo-scopic cameras. Int J Ind Electron Electr Eng 4(5):47–51
Rasheed A, Kamal K, Zafar T, Mathavan S, Rahman M (2015) Stabilization of 3d pavement images for pothole metrology using the Kalman filter. In: 2015 IEEE 18th International conference on intelligent transportation systems. IEEE, pp 2671–2676
Klette R (2014) Concise computer vision. Springer, Berlin
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Lin G, Milan A, Shen C, Reid I (2017) Refinenet: multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1925–1934
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
Staniek M (2015) Neural networks in stereo vision evaluation of road pavement condition. In: Proceedings of international symposium on non-destructive testing civil engineering, pp. 15–17
Cyganek B, Siebert JP (2011) An introduction to 3D computer vision techniques and algorithms. Wiley, London
He K, Gkioxari G, Dollaár P, Girshick R (2017) Mask r-cnn
Jiang R (2018) Understanding-mask rcnn. https://ronjian.github.io/blog/2018/05/16/Understand-Mask-RCNN. Accessed 30 May 2020
Pothole dataset. http://augmentedstartups.info/potholedataset. Accessed: 30 Jan 2020
Kolomeychenko M (2019) Supervisely platform. https://supervise.ly. Accessed 2 Dec 2019
Lin TY, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, Perona P, Ramanan D, Zitnick CL, Dollár P (2014) Microsoft coco: common objects in context
Abadi M et al (2015) TensorFlow: large-scale machine learning on heterogeneous systems. http://tensorflow.org/. Software available from tensorflow.org
Dhiman A, Klette R (2019) Pothole detection using computer vision and learning. IEEE Trans Intell Transport Syst
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Kavati, I. (2022). Deep Learning-Based Pothole Detection for Intelligent Transportation Systems. In: Patgiri, R., Bandyopadhyay, S., Borah, M.D., Emilia Balas, V. (eds) Edge Analytics. Lecture Notes in Electrical Engineering, vol 869. Springer, Singapore. https://doi.org/10.1007/978-981-19-0019-8_20
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DOI: https://doi.org/10.1007/978-981-19-0019-8_20
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