IMAGE-BASED DETECTION OF SURFACE DAMAGE ON SHIP HULLS USING YOLOV8, FASTER R-CNN, AND SSD
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Choirul Mufit(1*)
Politeknik Perkapalan Negeri Surabaya
https://orcid.org/0000-0002-9275-1973
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Lilik Khoiriyah(2)
Politeknik Perkapalan Negeri Surabaya
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Firly Irhamni Ahsan(3)
Politeknik Perkapalan Negeri Surabaya
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Aslikha Amalia(4)
Politeknik Perkapalan Negeri Surabaya
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Nakeyzatria Candra Suryana(5)
Politeknik Perkapalan Negeri Surabaya
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M Rizqi Mubarok(6)
Politeknik Perkapalan Negeri Surabaya
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Yulia Ayu Nastiti(7)
Politeknik Perkapalan Negeri Surabaya
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Vena Rizqi Pusparani(8)
Politeknik Perkapalan Negeri Surabaya
(*) Corresponding Author
Abstract
Corrosion on ship hull surfaces poses a critical challenge in the maritime industry, directly affecting structural safety, fuel efficiency, and maintenance costs. Conventional manual visual inspection at drydocks is time-consuming, expensive, and poses potential safety hazards to inspectors. This study aims to develop an image-based ship hull corrosion detection system and objectively compare the performance of three deep learning architectures—YOLOv8, Faster R-CNN, and SSD300—under identical experimental conditions. The dataset combines the Marine Vessel Corrosion Dry-Dock Dataset (MaVeCoDD) with self-collected hull images, yielding a total of 76 images. Data were split consistently into training, validation, and testing subsets. Each model was trained via transfer learning from COCO pretrained weights and evaluated using mAP50, Precision, Recall, and F1-Score on the identical test set. Experimental results demonstrate that YOLOv8n outperforms the other architectures, achieving an mAP50 of 0.131, Precision of 0.313, Recall of 0.226, and F1-Score of 0.263. Faster R-CNN attained the highest Precision (0.651) but suffered from a low Recall (0.088), whereas SSD300 yielded the lowest performance with an mAP50 of 0.003. In conclusion, YOLOv8 proves to be the most suitable architecture for ship hull corrosion detection due to its superior balance between Precision and Recall compared to Faster R-CNN and SSD300.
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References
United Nations Conference on Trade and Development, “Review of Maritime Transport 2023,” Geneva, 2023. [Online]. Available: https://unctad.org/publication/review-maritime-transport-2023
R. E. Melchers and R. Jeffrey, “Probabilistic Models for Steel Corrosion Loss and Pitting of Marine Infrastructure,” Reliab. Eng. Syst. Saf., vol. 93, no. 3, pp. 423–432, Mar. 2008, doi: 10.1016/j.ress.2006.12.006.
F. Bonnin-Pascual and A. Ortiz, “On the Use of Robots and Vision Technologies for the Inspection of Vessels: A Survey on Recent Advances,” Ocean Engineering, vol. 190, p. 106420, Oct. 2019, doi: 10.1016/j.oceaneng.2019.106420.
Y. Yao, Y. Yang, Y. Wang, and X. Zhao, “Artificial Intelligence-Based Hull Structural Plate Corrosion Damage Detection and Recognition Using Convolutional Neural Network,” Applied Ocean Research, vol. 90, p. 101823, Sep. 2019, doi: 10.1016/j.apor.2019.05.008.
O. Mursida, S. Adam, A. R. Prabowo, and H. Nubli, “Artificial Intelligence-Based Ship Hull Plate Corrosion Monitoring Using Convolutional Neural Network (CNN): Comparison of YOLOv8 and Detectron2 Architecture Models,” Ship Technology Research, vol. 72, no. 2, pp. 88–98, 2024, doi: 10.1080/09377255.2024.2397608.
K. Kamzelis, G. Chliveros, and I. Tzanetatos, “MaVeCoDD: Marine Vessel Corrosion Dry-Dock Images Dataset,” 2021, Mendeley Data. doi: 10.17632/ry392rp8cj.1.
G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics YOLOv8,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” Adv. Neural Inf. Process. Syst., vol. 28, pp. 91–99, 2015.
W. Liu et al., “SSD: Single Shot MultiBox Detector,” Proceedings of the European Conference on Computer Vision (ECCV), pp. 21–37, 2016, doi: 10.1007/978-3-319-46448-0_2.
Z. Ameli, S. J. Nesheli, and E. N. Landis, “Deep Learning-Based Steel Bridge Corrosion Segmentation and Condition Rating Using Mask R-CNN and YOLOv8,” Infrastructures (Basel)., vol. 9, no. 1, p. 3, Jan. 2024, doi: 10.3390/infrastructures9010003.
J.-A. Kim, J.-Y. Sung, and S.-H. Park, “Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition,” in Proceedings of the IEEE International Conference on Consumer Electronics – Asia (ICCE-Asia), Seoul, Korea, 2020, pp. 1–4. doi: 10.1109/ICCE-Asia49877.2020.9277040.
G. Chliveros and others, “A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module,” Coatings, vol. 14, no. 6, p. 768, Jun. 2024, doi: 10.3390/coatings14060768.
J. Terven, D.-M. Córdova-Esparza, and J.-A. Romero-González, “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Mach. Learn. Knowl. Extr., vol. 5, no. 4, pp. 1680–1716, 2023, doi: 10.3390/make5040083.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature Pyramid Networks for Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 2117–2125. doi: 10.1109/CVPR.2017.106.
Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye, “Object Detection in 20 Years: A Survey,” Proceedings of the IEEE, vol. 111, no. 3, pp. 257–276, Mar. 2023, doi: 10.1109/JPROC.2023.3238524.
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