A Novel Technology Stack for Automated Road Quality Assessment Framework using Deep Learning Techniques
Abstract
Road infrastructure plays a pivotal role in supporting societal, economic, and cultural progress. The capacity of a road refers to its ability to handle vehicular volume. Inadequate road capacity and the presence of defects like potholes and cracks result in suboptimal travel conditions and pose significant safety risks for drivers, cyclists, and pedestrians. The regular evaluation of these road quality aspects is essential for effective maintenance. However, current methods for assessing road capacity are time-consuming, subjective, and heavily reliant on manual labor. Moreover, existing deep learning-based approaches for detecting road defects often lack accuracy. To overcome these challenges, a fully automated and accurate system for evaluating road quality is imperative. Thus, the objective of this research work is to propose a novel technology stack for a comprehensive Automated Road Quality Assessment (ARQA) framework designed to assess road quality. The experimental findings demonstrate that the suggested vehicle detection and pothole detection methods work effectively and exhibit enhancements of 18% and 6%, respectively, in comparison to existing approaches.
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References
J. Azimjonov, and A. Ozmen, A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways, Journal of Advanced Engineering Informatics, Vol. 50, pp. 1-12, 2021. DOI: https://doi.org/10.1016/j.aei.2021.101393
R. Dhatbale and BR. Chilukuri, Deep Learning Techniques for Vehicle Trajectory Extraction in Mixed Traffic. Journal of Big Data Analytics in Transportation, Vol. 3, pp. 141-57, 2021. DOI: https://doi.org/10.1007/s42421-021-00042-3
Y. He, Z. Pan, L. Li, Y. Shan, D. Cao, and L. Chen, Real-time vehicle detection from short-range aerial image with compressed mobilenet, Proceeding of the International Conference on Robotics and Automation, Montreal, Canada, pp. 8339-8345, 2019. DOI: https://doi.org/10.1109/ICRA.2019.8793673
S. Huang, Y. He, and XA. Chen, M-YOLO: A Nighttime Vehicle Detection Method Combining Mobilenet v2 and YOLO v3, Journal of Physics: Conference Series, Vol. 1883, pp. 1-6, 2021.
K. Dudekula, R. Kiran, M. Reddy, R. Shet, R. Rohan, and JS. Vishwas, Determination of Level of Service and Traffic Flow Characteristics for a Selected Arterial Roads in Bangalore City, pp. 1-5, 2021.
M. Khanam, and M. Marufuzzaman, Determination of the Level of Service at Major Intersection: A Case Study on Traffic More Intersection Area, Pabna, Bangladesh, Proceeding of the International Conference on Planning, Architecture and Civil Engineering, Bangladesh, pp. 1-6, 2019.
J. Raj, and P. Vedagiri, Evaluation of Perception and Non Perception based Approaches for Modeling Urban Road Level of Service, Journal of The Institution of Engineers: Series A, Vol. 103, pp. 467-80, 2022.
S. Volosenko and A. Laurinavicius, Level of service evaluation methods: possible adaptation for Lithuania, The Baltic journal of road and bridge engineering, Vol. 15, pp.145-57, 2020.
AK. Pandey, R. Iqbal, T. Maniak, C. Karyotis, S. Akuma, and V. Palade, Convolution neural networks for pothole detection of critical road infrastructure, Journal of Computers and Electrical Engineering, Vol. 99, pp. 1-12, 2022.
B. Varona, A. Monteserin, and A. Teyseyre, A deep learning approach to automatic road surface monitoring and pothole detection, Journal of Personal and Ubiquitous Computing, Vol. 24, pp. 519-34, 2022.
C. Saisree, and U. Kumaran, Pothole Detection Using Deep Learning Classification Method, Procedia Computer Science. Vol. 218, pp. 2143-52, 2023. DOI: https://doi.org/10.1016/j.procs.2023.01.190
A. Dhiman, and R. Klette, Pothole Detection Using Computer Vision and Learning, Journal of IEEE Transactions on Intelligent Transportation Systems, Vol. 21, pp. 3536-3550, 2020. DOI: https://doi.org/10.1109/TITS.2019.2931297
SJ. Wang, JK, Zhang, and XQ, Lu, Research on Real-Time Detection Algorithm for Pavement Cracks Based on SparseInst-CDSM, Mathematics. Vol. 11, pp. 1-20, 2023. DOI: https://doi.org/10.3390/math11153277
Z. Lv, C. Cheng, and H. Lv, Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model, Philosophical Transactions of the Royal Society A., Vol. 381, pp. 1-15, 2023. DOI: https://doi.org/10.1098/rsta.2022.0169
L. Deng, A. Zhang, J. Guo, Y. Liu, An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds, Remote Sensing, Vol. 10, pp. 1-18, 2023. DOI: https://doi.org/10.3390/rs15061530
YM Kim, YG Kim, SY Son, SY Lim, BY Choi, DH Choi, Review of Recent Automated PotholeDetection Methods, Journal of Applied Sciences, Vol. 12, pp. 1-15, 2022. DOI: https://doi.org/10.3390/app12115320
J Raj, P VedagiriP, Evaluation of Perception and Nonperception based Approaches for Modeling Urban Road Level of Service, Journal of The Institution of Engineers (India): Series A. Vol. 103, No. 2, pp. 467-80, 2022. DOI: https://doi.org/10.1007/s40030-021-00602-4
S Volosenko, A Laurinavicius, Level of service evaluation methods: possible adaptation for Lithuania. The Baltic journal of road and bridge engineering. Vol. 15, No. 2, pp. 145-57, 2020. DOI: https://doi.org/10.7250/bjrbe.2020-15.477
R Fan, MJ 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 Intelligent Vehicles Symposium, pp. 474-479. 2019. DOI: https://doi.org/10.1109/IVS.2019.8814000
H Maeda, T Kashiyama, Y Sekimoto, T Seto, H Omata, Generative adversarial network for road damage detection. Computer-Aided Civil and Infrastructure Engineering, Vol. 36, No. 1, pp. 47-60, 2021. DOI: https://doi.org/10.1111/mice.12561
S Naddaf-Sh, MM Naddaf-Sh, AR Kashani, H Zargarzadeh. An efficient and scalable deep learning approach for road damage detection. IEEE International Conference on Big Data (Big Data), pp. 5602-5608, 2020. DOI: https://doi.org/10.1109/BigData50022.2020.9377751
AK Pandey, R Iqbal, T Maniak, C Karyotis, S Akuma, V Palade, Convolution neural networks for pothole detection of critical road infrastructure. Computers and Electrical Engineering, Vol. 99, pp. 107725, 2022. DOI: https://doi.org/10.1016/j.compeleceng.2022.107725
K Dudekula, R Kiran, M Reddy, RR Shet, JS Vishwas, R Rohan, Determination of Level of Service and Traffic Flow Characteristics for a Selected Arterial Roads in Bangalore City, pp. 1- 5, 2021 DOI: https://doi.org/10.2139/ssrn.3830443
S Huang, Y He, XA Chen, M-YOLO: A Nighttime Vehicle Detection Method Combining Mobilenet v2 and YOLO v3, Journal of Physics: Conference Series, Vol. 1883, No. 1, pp. 012094), 2021. DOI: https://doi.org/10.1088/1742-6596/1883/1/012094
N Abulizi, A Kawamura, K Tomiyama, S Fujita, Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS, Journal of Traffic and Transportation Engineering (English Edition), Vol. 3, No. 5, pp. 398-411, 2016. DOI: https://doi.org/10.1016/j.jtte.2016.09.004
B Varona, A Monteserin, A Teyseyre, A deep learning approach to automatic road surface monitoring and pothole detection, Personal and Ubiquitous Computing, Vol. 24 No. 4, pp. 519-34, 2020. DOI: https://doi.org/10.1007/s00779-019-01234-z
FX Ferdinandus FX, EI Setiawan, EM Yuniarno, MH Purnomo, 3D Visualization for Lung Surface Images of Covid-19 Patients based on U-Net CNN Segmentation, EMITTER International Journal of Engineering Technology. Vol. 10. No. 2, pp. 320-337, 2022. DOI: https://doi.org/10.24003/emitter.v10i2.709
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