Sistem Pendeteksian Rintangan untuk Kapal Tak Berawak dengan Kombinasi Deteksi Tepi, Transformasi Hough dan Deteksi Saliensi
Main Article Content
Abstract
Kapal tanpa awak yang digunakan untuk berpatroli di perairan pesisir membutuhkan sistem deteksi dan penghindar rintangan yang andal. Hal ini dikarenakan banyak terdapat objek-objek yang dapat menghalangi lajunya kapal di permukaan air. Tantangan dalam pengembangan sistem pendeteksian rintangan berbasis citra bagi kapal tak berawak ukuran kecil adalah performa komputasi yang terbatas. Penelitian dilakukan untuk membuat sebuah sistem pendeteksian rintangan bagi kapal tanpa awak yang akurat dengan proses komputasi yang minimum agar dapat diimplementasikan ke dalam sistem tertanam. Sistem yang dibuat mengkombinasikan deteksi tepi, transformasi Hough dan deteksi saliensi untuk mendeteksi adanya rintangan di atas permukaan air. Dari hasil pengujian didapatkan bahwa performa sistem dapat mendekati kemampuan sistem lain yang lebih kompleks namun belum dapat mengunggulinya. Sistem ini juga diimplementasikan pada sistem tertanam dan menghasilkan kecepatan yang memungkinkan untuk dilakukan pengimplementasian secara real-time.
Article Details
Copyright Notice
Authors who publish with Journal of Informatics, Information System, Software Engineering and Applications (INISTA) agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
References
[2] D. Hardianto dan W. D. Aryawan, “Pembuatan Konsep Desain Unmanned Surface Vehicle (USV) untuk Monitoring Wilayah Perairan Indonesia,” J. Tek. ITS, vol. 6, no. 2, hlm. G65–G70, Sep 2017, doi: 10.12962/j23373539.v6i2.23366.
[3] C. Barrera, I. Padron, F. S. Luis, dan O. Llinas, “Trends and challenges in unmanned surface vehicles (USV): From survey to shipping,” TransNav Int. J. Mar. Navig. Saf. Sea Transp., vol. Vol. 15 No. 1, 2021, doi: 10.12716/1001.15.01.13.
[4] A. Gonzalez-Garcia dan H. Castañeda, “Guidance and Control Based on Adaptive Sliding Mode Strategy for a USV Subject to Uncertainties,” IEEE J. Ocean. Eng., vol. 46, no. 4, hlm. 1144–1154, Okt 2021, doi: 10.1109/JOE.2021.3059210.
[5] D. Sousa, M. Luís, S. Sargento, dan A. Pereira, “An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network,” Sensors, vol. 18, no. 10, hlm. 3440, Okt 2018, doi: 10.3390/s18103440.
[6] L. Ma, W. Xie, H. Huang, dan School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China, “Convolutional neural network based obstacle detection for unmanned surface vehicle,” Math. Biosci. Eng., vol. 17, no. 1, hlm. 845–861, 2020, doi: 10.3934/mbe.2020045.
[7] W. Wang dan X. Luo, “Autonomous Docking of the USV using Deep Reinforcement Learning Combine with Observation Enhanced,” dalam 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Agu 2021, hlm. 992–996. doi: 10.1109/AEECA52519.2021.9574371.
[8] S. Sembiring dan K. Exaudi, “Perancangan Robot Kapal dengan Perilaku Menghindari Rintangan,” KNTIA, vol. 4, no. 2017, hlm. A117–A124, 2017.
[9] B. M. Pratama, D. Gunawan, dan R. A. G. Gultom, “Deep learning-based object detection and geographic coordinate estimation system for GeoTiff imagery,” J. Phys. Conf. Ser., vol. 1577, no. 1, hlm. 012003, Jul 2020, doi: 10.1088/1742-6596/1577/1/012003.
[10] J. Villa, J. Aaltonen, dan K. T. Koskinen, “Path-Following With LiDAR-Based Obstacle Avoidance of an Unmanned Surface Vehicle in Harbor Conditions,” IEEEASME Trans. Mechatron., vol. 25, no. 4, hlm. 1812–1820, Agu 2020, doi: 10.1109/TMECH.2020.2997970.
[11] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, hlm. 679–698, Nov 1986, doi: 10.1109/TPAMI.1986.4767851.
[12] L. A. F. Fernandes dan M. M. Oliveira, “Real-time line detection through an improved Hough transform voting scheme,” Pattern Recognit., vol. 41, no. 1, hlm. 299–314, Jan 2008, doi: 10.1016/j.patcog.2007.04.003.
[13] R. Achanta, S. Hemami, F. Estrada, dan S. Susstrunk, “Frequency-tuned salient region detection,” dalam 2009 IEEE conference on computer vision and pattern recognition, 2009, hlm. 1597–1604.
[14] T.-Y. Lin dkk., “Microsoft COCO: Common Objects in Context,” dalam Computer Vision – ECCV 2014, Cham, 2014, hlm. 740–755. doi: 10.1007/978-3-319-10602-1_48.
[15] K. M. Ting, “Precision and Recall,” dalam Encyclopedia of Machine Learning, C. Sammut dan G. I. Webb, Ed. Boston, MA: Springer US, 2010, hlm. 781–781. doi: 10.1007/978-0-387-30164-8_652.
[16] M. Kristan, V. Sulić Kenk, S. Kovačič, dan J. Perš, “Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles,” IEEE Trans. Cybern., vol. 46, no. 3, hlm. 641–654, Mar 2016, doi: 10.1109/TCYB.2015.2412251.