Perancangan Sistem Deteksi Tingkat Kemiringan Jalan Sederhana Dengan Metode Otsu Thresholding Menggunakan Colab
DOI:
https://doi.org/10.62951/router.v2i3.156Keywords:
Road Slope Detection, Otsu Thresholding, Image Processing, Road Safety, Transportation Infrastructure, Observation MethodAbstract
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
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
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Router : Jurnal Teknik Informatika dan Terapan

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.