Perancangan Sistem Deteksi Tingkat Kemiringan Jalan Sederhana Dengan Metode Otsu Thresholding Menggunakan Colab

Authors

  • Bagus Nurhannudin Universitas Muhammadiyah Ponorogo

DOI:

https://doi.org/10.62951/router.v2i3.156

Keywords:

Road Slope Detection, Otsu Thresholding, Image Processing, Road Safety, Transportation Infrastructure, Observation Method

Abstract

This research focuses on the development of a simple system for detecting road slopes using the Otsu Thresholding method. The primary objective is to create an effective and efficient system capable of identifying different road slope levels accurately. The system utilizes image processing techniques, where the Otsu Thresholding method is applied to differentiate between road surfaces and surrounding environments. By analyzing these images, the system determines the degree of road slope. This study emphasizes the importance of accurate road slope detection for improving road safety and maintenance. The observation method was employed in this research to gather relevant data and evaluate the system's performance. The results demonstrate that the designed system is capable of effectively detecting various road slope levels, providing a valuable tool for transportation infrastructure monitoring.

Downloads

Download data is not yet available.

References

Ali, S., & Abbas, M. (2021). Hybrid method for road crack detection using Otsu's thresholding and morphological image processing. Journal of Intelligent & Fuzzy Systems, 41(3), 1795-1805. https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs1795

Chatterjee, S., & Sengupta, A. (2019). Application of image processing techniques for road damage detection: A comparative study. Journal of Transportation Engineering, Part B: Pavements, 145(3), 05019004. https://ascelibrary.org/doi/10.1061/JPEODX.0000214

Chen, H., Liu, C., & Wang, Q. (2021). Deep learning-based road crack detection using multi-scale image fusion. IEEE Transactions on Intelligent Transportation Systems, 22(5), 3161-3172. https://doi.org/10.1109/TITS.2020.3041739

Chen, X., Liu, J., Wang, H., & Li, Q. (2021). Adaptive Canny and semantic segmentation networks for road crack detection. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3053497

Fahriah, U., Iswandari, D. N., & Ulfa, M. I. (2023). Gambaran pengetahuan remaja tentang program pelayanan kesehatan peduli remaja (PKPR) di MAN 1 Amuntai. Health Research Journal of Indonesia, 1(6), 249-253. https://doi.org/10.63004/hrji.v1i6.220

Hossain, M. S., & Wang, S. (2020). Automatic road crack detection using convolutional neural networks with dual loss functions. Journal of Computing in Civil Engineering, 34(4), 04020020. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000899

Khadangi, A., & Rahmani, R. (2020). Deep learning-based road condition monitoring using UAVs. Remote Sensing, 12(8), 1258. https://www.mdpi.com/2072-4292/12/8/1258

Kim, J., & Lee, S. (2021). Robust road crack detection using multi-layer convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 36(3), 256-270. https://doi.org/10.1111/mice.12599

Li, B., Zhang, C., & Wang, T. (2021). Vision-based road slope estimation using road lines or local features. IET Digital Library. https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2020.0256

Nguyen, H. T., & Tran, Q. V. (2019). A new method for road surface crack detection using image processing and machine learning. Journal of Computational and Applied Mathematics, 362, 13-23. https://doi.org/10.1016/j.cam.2019.05.028

Nurbadlina, R. F., Shaluhiyah, Z., & Suryoputro, A. (2022). Collaboration across sectors of adolescent reproductive health education assisted by the Semarang City Social Service. Jurnal Kebidanan, 12(1), 1-7. https://doi.org/10.31983/jkb.v12i1.7995

Nurranti, N., & Werdani, E. K. (2024). Hubungan dukungan tenaga kesehatan dengan pemanfaatan PKPR (pelayanan kesehatan peduli remaja) di SMA Batik 1 Surakarta. Universitas Muhammadiyah Surakarta. Retrieved from http://eprints.ums.ac.id/id/eprint/122536

Pham, Q. T., & Luong, N. C. (2019). Road defect detection using machine learning: A comparative study. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2019.07.008

Ren, Z., Yu, X., & Ma, Y. (2022). Automatic road surface defect detection using UAV images and a deep learning approach. Remote Sensing, 14(2), 412. https://www.mdpi.com/2072-4292/14/2/412

Roy, S., & Goswami, R. (2020). Automated road crack detection using U-Net based segmentation approach. IEEE Access, 8, 146136-146147. https://doi.org/10.1109/ACCESS.2020.3015041

Setiawati, I., Zainiyah, Z., & Zainiyah, H. (2023). Optimalisasi edukasi kesehatan reproduksi remaja (PHBS). Jurnal Pengabdian Kepada Masyarakat, 7(3), 41-47. https://doi.org/10.30787/gemassika.v7i1.783

Suh, Y. J., & Cho, D. (2019). Road surface defect detection using laser and image processing techniques. Journal of Visual Communication and Image Representation, 62, 102648. https://doi.org/10.1016/j.jvcir.2019.102648

Tang, X., & Li, J. (2021). Road crack detection based on CNN using multi-spectral images. Construction and Building Materials, 287, 122975. https://doi.org/10.1016/j.conbuildmat.2021.122975

Winarni, S., Tsamaradhia, T. A., & Rusdhianata, P. A. (2023). Pemberdayaan masyarakat dalam optimalisasi pelayanan kesehatan peduli remaja (PKPR) melalui posyandu remaja di Desa Teluk Awur. Journal of Public Health and Community Services, 2(1), 23-25. https://doi.org/10.14710/jphcs.2023.17192

Xu, Y., & Yan, Y. (2019). Crack detection on road pavement using a morphological edge detector. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04019001. https://doi.org/10.1061/JPEODX.0000201

Yuliani, M., Yufinah, Y., & Maesaroh, M. (2021). Gambaran pembentukan kader dan pelaksanaan posyandu remaja dalam upaya peningkatan kesehatan reproduksi remaja. Jurnal Pengabdian Masyarakat, 4(2), 266. https://doi.org/10.31764/jpmb.v4i2.4157

Zhang, Y., Liu, X., & Sun, Z. (2020). Road pavement condition assessment using image processing and machine learning. Sensors, 20(14), 3947. https://www.mdpi.com/1424-8220/20/14/3947

Zhao, Y., Chen, L., & Guo, Z. (2020). A novel approach for road surface crack detection using an improved Otsu method and multi-layer filtering. Applied Sciences, 10(11), 3976. https://www.mdpi.com/2076-3417/10/11/3976

Published

2024-07-25

How to Cite

Bagus Nurhannudin. (2024). Perancangan Sistem Deteksi Tingkat Kemiringan Jalan Sederhana Dengan Metode Otsu Thresholding Menggunakan Colab. Router : Jurnal Teknik Informatika Dan Terapan, 2(3), 137–146. https://doi.org/10.62951/router.v2i3.156

Similar Articles

1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.