Main Article Content

Abstract

Pada era teknologi yang terus berkembang, penggunaan drone untuk pengiriman barang, termasuk makanan, menjadi salah satu inovasi dalam logistik modern. Penelitian ini berfokus pada pengembangan sistem pendeteksi target drop-off pada Autonomous Drone Food Delivery (ADFLY) menggunakan citra objek berwarna. Dalam penelitian ini, digunakan metode pemrosesan citra berbasis warna, khususnya merah, untuk mendeteksi target drop-off yang memberikan kontras tinggi dengan latar belakang alam. Sistem ini dirancang menggunakan Raspberry Pi sebagai pengolah citra dan webcam beresolusi Full HD sebagai alat penangkap gambar secara real-time. Hasil pengujian menunjukkan bahwa jarak optimal untuk akurasi deteksi adalah 120 cm, sementara kondisi pencahayaan dan warna objek sekitar dapat mempengaruhi kinerja deteksi. Keterbatasan pada kemampuan sensor kamera untuk membedakan warna tertentu, seperti merah dan magenta, menjadi tantangan dalam lingkungan yang bervariasi. Untuk peningkatan lebih lanjut, disarankan integrasi algoritma deep learning dan pemilihan kamera dengan sensitivitas sensor lebih tinggi untuk meningkatkan keandalan dalam kondisi pencahayaan yang dinamis. Studi ini membuka potensi peningkatan akurasi sistem ADFLY dalam industri pengiriman berbasis drone.

Keywords

drone pendeteksi objek target drop-off pemprosesan citra

Article Details

How to Cite
Yuliansyah, H., & Putri Sulistiawani, M. (2024). Pengembangan Sistem Deteksi Target Drop-Off Berbasis Warna pada Autonomous Drone Food Delivery. JURNAL AMPLIFIER : JURNAL ILMIAH BIDANG TEKNIK ELEKTRO DAN KOMPUTER, 14(2), 155–160. https://doi.org/10.33369/jamplifier.v14i2.38114

References

  1. R. Goyal, “Drone Delivery System: An Insight,” Int. J. Comput. Appl., vol. 178, no. 33, pp. 9–13, May 2019.
  2. K. W. Tam, C. T. Wang, and Y. Wang, “The application of drone in food delivery,” in Proc. Int. Conf. Indust. Eng. Eng. Manag., Dec. 2021, pp. 387–391.
  3. H. C. Huang and Y. P. Cheng, “The potential of GPS limitations in autonomous delivery drones,” IEEE Trans. Veh. Technol., vol. 67, no. 3, pp. 2549–2563, Mar. 2018.
  4. D. H. Ballard and C. M. Brown, Computer Vision. Englewood Cliffs, NJ, USA: Prentice-Hall, 1982.
  5. S. Monk, Programming the Raspberry Pi: Getting Started with Python, 2nd ed. New York, NY, USA: McGraw-Hill, 2015.
  6. J. L. Leitner, “Automation in Food Delivery Systems: Utilizing Autonomous Vehicles,” Int. J. Innov. Res. Sci. Technol., vol. 5, no. 4, pp. 112–120, Apr. 2020.
  7. R. C. Gonzalez dan R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ, USA: Prentice-Hall, 2008.
  8. S. Sridhar, Digital Image Processing. New York, NY, USA: Oxford University Press, 2011.
  9. R. Szeliski, Computer Vision: Algorithms and Applications. London, U.K.: Springer, 2011.
  10. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 779–788.
  11. D. H. Ballard and C. M. Brown, Computer Vision. Englewood Cliffs, NJ, USA: Prentice-Hall, 1982.
  12. A. Koschan and M. Abidi, Digital Color Image Processing. Hoboken, NJ, USA: Wiley, 2008.
  13. S. Monk, Programming the Raspberry Pi: Getting Started with Python, 2nd ed. New York, NY, USA: McGraw-Hill, 2015.
  14. M. Banzi and M. Shiloh, Getting Started with Raspberry Pi, 3rd ed. Sebastopol, CA, USA: O’Reilly Media, 2021.
  15. P. S. Heckbert, "Fundamentals of texture mapping and image warping," M.S. thesis, Dept. Elect. Eng. Comput. Sci., Univ. California, Berkeley, CA, USA, 1989.
  16. A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging. Philadelphia, PA, USA: SIAM, 2001.
  17. Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  18. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Adv. Neural Inf. Process. Syst., 2012, pp. 1097–1105.
  19. S. Monk, Programming the Raspberry Pi: Getting Started with Python, 2nd ed. New York, NY, USA: McGraw-Hill, 2015.
  20. M. Banzi and M. Shiloh, Getting Started with Raspberry Pi, 3rd ed. Sebastopol, CA, USA: O’Reilly Media, 2021.
  21. P. S. Heckbert, "Fundamentals of texture mapping and image warping," M.S. thesis, Dept. Elect. Eng. Comput. Sci., Univ. California, Berkeley, CA, USA, 1989.
  22. J. L. Leitner, “Automation in Food Delivery Systems: Utilizing Autonomous Vehicles,” Int. J. Innov. Res. Sci. Technol., vol. 5, no. 4, pp. 112–120, Apr. 2020.
  23. A. Koschan and M. Abidi, Digital Color Image Processing. Hoboken, NJ, USA: Wiley, 2008.
  24. G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library. Sebastopol, CA, USA: O’Reilly Media, 2008.
  25. D. H. Ballard and C. M. Brown, Computer Vision. Englewood Cliffs, NJ, USA: Prentice-Hall, 1982.
  26. A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging. Philadelphia, PA, USA: SIAM, 2001.
  27. H. C. Huang and Y. P. Cheng, “The potential of GPS limitations in autonomous delivery drones,” IEEE Trans. Veh. Technol., vol. 67, no. 3, pp. 2549–2563, Mar. 2018.
  28. Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.