iRodd (intelligent-road damage detection) for real-time infrastructure preservation in detection, classification, calculation, and visualization
Vol 8, Issue 11, 2024
VIEWS - 1176 (Abstract)
Abstract
Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).
Keywords
Full Text:
PDFReferences
- Abbas, I. H., & Ismael, M. Q. (2021). Automated Pavement Distress Detection Using Image Processing Techniques. Engineering, Technology and Applied Science Research, 11(5), 7702–7708. https://doi.org/10.48084/etasr.4450
- Al Duhayyim, M., Malibari, A. A., Alharbi, A., et al. (2022). Road Damage Detection Using the Hunger Games Search with Elman Neural Network on High-Resolution Remote Sensing Images. Remote Sensing, 14(24). https://doi.org/10.3390/rs14246222
- Alamgeer, M., Alkahtani, H. K., Maashi, M., et al. (2023). Optimal Fuzzy Wavelet Neural Network Based Road Damage Detection. IEEE Access, 11, 61986–61994. https://doi.org/10.1109/ACCESS.2023.3283299
- Alqethami, S., Alghamdi, S., Alsubait, T., & Alhakami, H. (2022). RoadNet: Efficient Model to Detect and Classify Road Damages. Applied Sciences (Switzerland), 12(22). https://doi.org/10.3390/app122211529
- Amelia Setiaputri, H., Isradi, M., Irfan Rifai, A., et al. (2021). Analysis Of Urban Road Damage With Pavement Condition Index (PCI) And Surface Distress Index (SDI) Methods. ADRI International Journal of Sciences, Engineering and Technology, 6(01), 10–19. https://doi.org/10.29138/ijset.v6i01.61
- Benallal, M. A., & Tayeb, M. S. (2023). An image-based convolutional neural network system for road defect detection. IAES International Journal of Artificial Intelligence, 12(2), 577–584. https://doi.org/10.11591/ijai.v12.i2.pp577-584
- Benmhahe, B., & Chentoufi, J. A. (2021). Automated Pavement Distress Detection, Classification and Measurement: A Review. International Journal of Advanced Computer Science and Applications, 12(8), 708–718. https://doi.org/10.14569/IJACSA.2021.0120882
- Bera, K., Parthiban, R., & Karmakar, N. (2023). A Truly 3D Visible Light Positioning System Using Low Resolution High Speed Camera, LIDAR, and IMU Sensors. IEEE Access, 11, 98578–98585. https://doi.org/10.1109/ACCESS.2023.3312293
- Bhatt, U., Mani, S., Xi, E., & Kolter, J. Z. (2017). Intelligent Pothole Detection and Road Condition Assessment. arXiv, arXiv:1710.02595.
- Chaithavee, S., & Chayakul, T. (2022). Classification of 3D Point Cloud Data from Mobile Mapping System for Detecting Road Surfaces and Potholes using Convolution Neural Networks. International Journal of Geoinformatics, 18(6), 11–23. https://doi.org/10.52939/ijg.v18i6.2455
- Choiri, A., Yusuf, M. S., Sari, R. N., & Artanti, L. D. (2024). Comparison of road damage analysis using PCI method and Bina Marga method and the analysis of road improvement methods using the road pavement design manual (Case study: Citayam—Parung road). E3S Web of Conferences, 479, 07017. https://doi.org/10.1051/e3sconf/202447907017
- Coenen, T. B. J., & Golroo, A. (2017). A review on automated pavement distress detection methods. Cogent Engineering, 4(1), 1–23. https://doi.org/10.1080/23311916.2017.1374822
- Costantino, D., Vozza, G., Pepe, M., & Alfio, V. S. (2022). Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges. Applied System Innovation, 5(4). https://doi.org/10.3390/asi5040063
- Desai, J., Liu, J., Hainje, R., et al. (2021). Assessing vehicle profiling accuracy of handheld lidar compared to terrestrial laser scanning for crash scene reconstruction. Sensors, 21(23). https://doi.org/10.3390/s21238076
- Dollár, P., & Zitnick, C. L. (2015). Fast edge detection using structured forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1558–1570. https://doi.org/10.1109/TPAMI.2014.2377715
- Elmousalami, H. H. (2019). Intelligent methodology for project conceptual cost prediction. Heliyon, 5(5), e01625. https://doi.org/10.1016/j.heliyon.2019.e01625
- Elwahsh, H., Allakany, A., Alsabaan, M., et al. (2023). A Deep Learning Technique to Improve Road Maintenance Systems Based on Climate Change. Applied Sciences (Switzerland), 13(15). https://doi.org/10.3390/app13158899
- Eslami, E., & Yun, H. B. (2023). Comparison of deep convolutional neural network classifiers and the effect of scale encoding for automated pavement assessment. Journal of Traffic and Transportation Engineering (English Edition), 10(2), 258–275. https://doi.org/10.1016/j.jtte.2022.08.002
- Everingham, M., Van Gool, L., Williams, C. K. I., et al. (2010). The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision, 88(2), 303–338. https://doi.org/10.1007/s11263-009-0275-4
- Gavilán, M., Balcones, D., Marcos, O., et al. (2011). Adaptive road crack detection system by pavement classification. Sensors, 11(10), 9628–9657. https://doi.org/10.3390/s111009628
- Gollob, C., Ritter, T., Kraßnitzer, R., et al. (2021). Measurement of forest inventory parameters with Apple iPad Pro and integrated lidar technology. Remote Sensing, 13(16), 1–35. https://doi.org/10.3390/rs13163129
- Guerrieri, M., & Parla, G. (2022). Flexible and stone pavements distress detection and measurement by deep learning and low-cost detection devices. Engineering Failure Analysis, 141, 106714. https://doi.org/10.1016/j.engfailanal.2022.106714
- Huang, Y., Xu, X., He, Z., et al. (2023). A Lightweight Road Crack and Damage Detection Method Using Yolov5s for IoT Applications. In: Proceedings of the 2023 IEEE/CIC International Conference on Communications in China (ICCC). pp. 1–6. https://doi.org/10.1109/ICCC57788.2023.10233422
- King, F., Kelly, R., & Fletcher, C. G. (2022). Evaluation of LiDAR-Derived Snow Depth Estimates From the iPhone 12 Pro. IEEE Geoscience and Remote Sensing Letters, 19, 1–5. https://doi.org/10.1109/LGRS.2022.3166665
- Kulambayev, B., Beissenova, G., Katayev, N., et al. (2022). A Deep Learning-Based Approach for Road Surface Damage Detection. Computers, Materials and Continua, 73(2), 3403–3418. https://doi.org/10.32604/cmc.2022.029544
- Lang, H., Lu, J. J., Lou, Y., & Chen, S. (2020). Pavement Cracking Detection and Classification Based on 3D Image Using Multiscale Clustering Model. Journal of Computing in Civil Engineering, 34(5), 1–12. https://doi.org/10.1061/(ASCE)cp.1943-5487.0000910
- Liang, H., Lee, S. C., & Seo, S. (2022). Automatic Recognition of Road Damage Based on Lightweight Attentional Convolutional Neural Network. Sensors, 22(24). https://doi.org/10.3390/s22249599
- Liu, Y., Duan, M., Ding, G., et al. (2023). HE-YOLOv5s: Efficient Road Defect Detection Network. Entropy, 25(9), 1–16. https://doi.org/10.3390/e25091280
- Luetzenburg, G., Kroon, A., & Bjørk, A. A. (2021). Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences. Scientific Reports, 11(1), 1–9. https://doi.org/10.1038/s41598-021-01763-9
- Ma, D., Fang, H., Xue, B., et al. (2020). Intelligent detection model based on a fully convolutional neural network for pavement cracks. CMES—Computer Modeling in Engineering and Sciences, 123(3), 1267–1291. https://doi.org/10.32604/cmes.2020.09122
- Maeda, H., Sekimoto, Y., & Seto, T. (2016). An easy infrastructure management method using on-board smartphone images and citizen reports by deep neural network. In: Proceedings of the Second International Conference on IoT in Urban Space. https://doi.org/10.1145/2962735.2962738
- Majidifard, H., Jin, P., Adu-Gyamfi, Y., & Buttlar, W. G. (2020). Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses. Transportation Research Record: Journal of the Transportation Research Board, 2674(2), 328–339. https://doi.org/10.1177/0361198120907283
- Neelam Jaikishore, C., Podaturpet Arunkumar, G., Jagannathan Srinath, A., et al. (2022). Implementation of Deep Learning Algorithm on a Custom Dataset for Advanced Driver Assistance Systems Applications. Applied Sciences (Switzerland), 12(18). https://doi.org/10.3390/app12188927
- Pebrianto, T. D. (2023). Analysis damage pavement using Binamarga method on Lamongan-Gresik road STA 45+200-47+200. SONDIR, 7(2), 110–117. https://doi.org/10.36040/sondir.v7i2.5997
- Pebrianto, T. D., Tjendani, H. T., & Hartatik, N. (2023). Analysis Damage Pavement Using Binamarga Method on Lamongan-Gresik Road Sta 45+200-47+200. Innovative: Journal of Social Science Research, 3(1), 689–703. https://doi.org/10.31004/innovative.v3i1.4804
- Pham, V., Nguyen, D., & Donan, C. (2022). Road Damage Detection and Classification with YOLOv7. In: Proceedings of the 2022 IEEE International Conference on Big Data (Big Data). pp. 6416–6423. https://doi.org/10.1109/BigData55660.2022.10020856
- Ramesh, A., Nikam, D., Balachandran, V. N., et al. (2022). Cloud-Based Collaborative Road-Damage Monitoring with Deep Learning and Smartphones. Sustainability (Switzerland), 14(14), 1–21. https://doi.org/10.3390/su14148682
- Ruseruka, C., Mwakalonge, J., Comert, G., et al. (2023). Road Condition Monitoring Using Vehicle Built-in Cameras and GPS Sensors: A Deep Learning Approach. Vehicles, 5(3), 931–948. https://doi.org/10.3390/vehicles5030051
- Shan, J., Li, Z., Lercel, D., et al. (2023). Democratizing photogrammetry: an accuracy perspective. Geo-Spatial Information Science, 26(2), 175–188. https://doi.org/10.1080/10095020.2023.2178336
- Song, W., Jia, G., Zhu, H., et al. (2020). Automated pavement crack damage detection using deep multiscale convolutional features. Journal of Advanced Transportation, 2020, 1–11. https://doi.org/10.1155/2020/6412562
- Tang, Y., Li, K., & Wang, K. (2022). Research on intelligent detection of pavement damage based on CNN. Mathematical Models in Engineering, 8(4), 98–107. https://doi.org/10.21595/mme.2022.22918
- Thuyet, D. Q., Jomoto, M., Hirakawa, K., & Lei Swe, Y. L. (2022). Development of an Autonomous Road Surface Damage Inspection Program Using Deep Convolutional Neural Network. Journal of Japan Society of Civil Engineers, 10(1), 235–246. https://doi.org/10.2208/journalofjsce.10.1_235
- Tsuboki, Y., Kawakami, T., Matsumoto, S., et al. (2023). A Real-time Background Replacement System Based on Estimated Depth for AR Applications. Journal of Information Processing, 31, 758–765. https://doi.org/10.2197/ipsjjip.31.758
- Utaminingrum, F., Alqadri, A. M., Somawirata, I. K., et al. (2023). Feature selection of gray-level Cooccurrence matrix using genetic algorithm with Extreme learning machine classification for early detection of Pole roads. Results in Engineering, 20, 101437. https://doi.org/10.1016/j.rineng.2023.101437
- Vogt, J., Ilic, M., & Bogenberger, K. (2023). A mobile mapping solution for VRU Infrastructure monitoring via low-cost LiDAR-sensors. Journal of Location Based Services, 17(4), 389–411. https://doi.org/10.1080/17489725.2023.2238660
- Waliulu, Y. E. P. R., Suprobo, P., & Adi, T. J. W. (2023). Volume Calculation Accuracy and 3D Visualization of Flexible Pavement Damage Based on Low-cost LiDAR. In: Proceedings of the 2023 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS). pp. 109–113. https://doi.org/10.1109/AGERS61027.2023.10490615
- Wan, F., Sun, C., He, H., et al. (2022). YOLO-LRDD: a lightweight method for road damage detection based on improved YOLOv5s. Eurasip Journal on Advances in Signal Processing, 2022(1). https://doi.org/10.1186/s13634-022-00931-x
- Wang, N., Shang, L., & Song, X. (2023). A Transformer-Optimized Deep Learning Network for Road Damage Detection and Tracking. Sensors, 23(17), 1–18. https://doi.org/10.3390/s23177395
- Wang, P., Wang, C., Liu, H., et al. (2023). Research on Automatic Pavement Crack Recognition Based on the Mask R-CNN Model. Coatings, 13(2). https://doi.org/10.3390/coatings13020430
- Xhimitiku, I., Pascoletti, G., Zanetti, E. M., & Rossi, G. (2022). 3D shape measurement techniques for human body reconstruction. Acta IMEKO, 11(2), 1–8. https://doi.org/10.21014/acta_imeko.v11i2.1219
- Xin, H., Ye, Y., Na, X., et al. (2023). Sustainable Road Pothole Detection: A Crowdsourcing Based Multi-Sensors Fusion Approach. Sustainability (Switzerland), 15(8), 1–23. https://doi.org/10.3390/su15086610
- Yan, X., Liu, Z., Zhuang, Z., & Miao, Y. (2022). Defect Point Location Method of Civil Bridge Based on Internet of Things Wireless Communication. Journal of Electrical and Computer Engineering, 2022, 1–12. https://doi.org/10.1155/2022/8728397
- Yang, T., Liu, Z., Chen, Y., & Yu, Y. (2020). Real-time, Inexpensive, and Portable Measurement of Water Surface Velocity through Smartphone. Water, 12(12), 3358. https://doi.org/10.3390/w12123358
- Zhang, H., Wu, Z., Qiu, Y., et al. (2022). A New Road Damage Detection Baseline with Attention Learning. Applied Sciences (Switzerland), 12(15), 7549. https://doi.org/10.3390/app12157594
- Zhao, R., Huang, Y., Luo, H., et al. (2023). A Framework for Using UAVs to Detect Pavement Damage Based on Optimal Path Planning and Image Splicing. Sustainability, 15(3), 2182. https://doi.org/10.3390/su15032182
- Zimbelman, E. G., & Keefe, R. F. (2022). Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety. PLoS ONE, 17(12), e0278645. https://doi.org/10.1371/journal.pone.0278645
DOI: https://doi.org/10.24294/jipd.v8i11.6162
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Tri Joko Wahyu Adi, Priyo Suprobo, Yusroniya Eka Putri Rachman Waliulu
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.