Morphological Grayscale Pre-processing to SAR Images for Reducing Noise in Ship Detection Based on YOLOv8

Caturiani Pratidina, Decia Safira, Trisna Gelar, Heru Permana, Suprihanto Suprihanto, Nurjannah Syakrani, Cholid Fauzi

Abstract


The development of a ship detection system using SAR pictures loaded with noise poses issues for pictures Intelligence (IMINT). The YOLOv8 model is utilized for ship identification. The preprocessing approaches entail employing a fusion of grayscale morphology techniques and image restoration using a harmonic mean filter and a bandpass. This technique is designed to assess the effect of noise reduction to enhance the accuracy of detecting objects in SAR images. The preprocessing technique is categorized into two methods: basic grayscale morphology (GM1-GM6) and a fusion of image restoration with grayscale morphology (GHB1-GHB6). The model's performance is assessed using mAP and IoU criteria. This research discovered that ship objects were not detected successfully in the presence of several types of noise. These failures were attributed to factors such as tiny ship size, low picture quality, and inadequate preprocessing techniques for noise handling. The findings indicate a substantial enhancement in ship detection, specifically in synthetic aperture radar (SAR) images affected by sidelobe noise. There were noticeable enhancements in the accuracy of images that underwent preprocessing using GHB5. GHB5 employs a combination of image restoration, closure, and erosion techniques.

Keywords


Bandpass Filter; Grayscale Morphology; Harmonic Mean Filter; Ship Detection; YOLOv8

References


Alsalman, H. (2019). Enhancing Digital Mammogram Images using Bandpass Filters in Frequency Domain. 19(11), 107–113.

Choi, H., & Jeong, J. (2019). Speckle noise reduction technique for sar images using statistical characteristics of speckle noise and discrete wavelet transform. Remote Sensing, 11(10). https://doi.org/10.3390/rs11101184

Cumming, I. G., & Wong, F. H. (2005). Digital Processing of Synthetic Aperture Radar Data : Algorithms ad Implementation. Massachusetts. Artech House.

Gelar, T., Pangestu, M., Fikri, M., Taufik, N., Teguh, U., & Hutahaean, J. (2022). Pendeteksian Penggunaan Masker Berbasis Android dan YOLOv5 untuk Media Video Realtime pada Ruang Perkantoran. Jurnal Pendidikan Multimedia (Edsence), 4(2), 75–88. https://doi.org/10.17509/edsence.v4i2.52230

Gonzalez, R. C., & Woods, R. E. (Richard E. (2018). Digital image processing (4th Editio).

Han, X., Zhao, L., Ning, Y., & Hu, J. (2021). ShipYOLO: An Enhanced Model for Ship Detection. Journal of Advanced Transportation, 2021. https://doi.org/10.1155/2021/1060182

Hidayatullah, D. P. (2021). Buku Sakti Deep Learning. Stunning Vision AI Academy.

Hidayatullah, P. (2017). Pengolahan Citra Digital Teori dan Aplikasi Nyata.

Huang, H., Sun, D., Wang, R., Zhu, C., & Liu, B. (2020). Ship Target Detection Based on Improved YOLO Network. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/6402149

Hussain, M. (2024). YOLOv1 to v8: Unveiling Each Variant-A Comprehensive Review of YOLO. IEEE Access, 12(March), 42816–42833. https://doi.org/10.1109/ACCESS.2024.3378568

Ma, M., & Pang, H. (2023). SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection. Applied Sciences (Switzerland), 13(14). https://doi.org/10.3390/app13148161

Muhamad Itikap, S., Syahid Abdurrahman, M., Soewono, E. B., & Gelar, T. (2023). Geometry and Color Transformation Data Augmentation for YOLOV8 in Beverage Waste Detection. Journal of Software Engineering, Information and Communication Technology (SEICT), 4(2), 123–138.

Nashuha, S. H., Ali, E. Y. E., & Wijanto, H. (2016). Pemrosesan Raw Data Sar (Synthetic Aperture Radar) menjadi Sar Image Space. E-Proceeding of Engineering, 3(3), 4450–4457.

Onyedinma, E. G., & Onyenwe, I. E. (2023). Image Restoration: A Comparative Analysis of Image De noising Using Different Spatial Filtering Techniques. International Journal of Latest Technology in Engineering, Management,& Applied Science (IJLTEMAS), 12(9), 55–63. https://doi.org/10.51583/IJLTEMAS

Shabbir, Z., Sarosh, A., & Nayyer, M. (2019). Space technology applications for maritime intelligence, surveillance, and reconnaissance. Astropolitics, 17(2), 104–126. https://doi.org/10.1080/14777622.2019.1636634

Syam’ani, S.Hut., M. S. (2019). Dasar-dasar Teknologi SAR. PPIIG ULM.

Wei, S., Zeng, X., Qu, Q., Wang, M., Su, H., & Shi, J. (2020). HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation. IEEE Access, 8, 120234–120254. https://doi.org/10.1109/ACCESS.2020.3005861

Zhao, C., Fu, X., Dong, J., Qin, R., Chang, J., & Lang, P. (2022). SAR Ship Detection Based on End-to-End Morphological Feature Pyramid Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 4599–4611. https://doi.org/10.1109/JSTARS.2022.3150910




DOI: https://doi.org/10.17509/seict.v5i2.75970

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Journal of Software Engineering, Information and Communication Technology (SEICT)

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

Journal of Software Engineering, Information and Communicaton Technology (SEICT), 
(e-ISSN:2774-1699 | p-ISSN:2744-1656) published by Program Studi Rekayasa Perangkat Lunak, Kampus UPI di Cibiru.


 Indexed by.