Identification of Unknown Marine Debris by ROVs Using Deep Learning and Different Convolutional Neural Network Structures | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 8, Volume 52, Issue 1, January and February 2024, Page 36-51 PDF (1.68 MB) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2023.250095.1289 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mahmoud Assem  1; Ibrahim M. Hassab-Allah2; Mohamed E.H. Eltaib3; Mahmoud Abdelrahim 4
| ||||
| 1M.Sc. Graduate student, Mechatronics Engineering Dept., Faculty of Engineering, Assiut University, Assiut, Egypt | ||||
| 2Professor, Mechanical Engineering Dept., Faculty of Engineering, Assiut University, Assiut, Egypt. | ||||
| 3Assoc. Professor, Mechanical Engineering Dept., Faculty of Engineering, Kafrelsheikh University, Egypt. | ||||
| 4Assoc. Professor, Mechatronics Engineering Dept., Faculty of Engineering, Assiut University, Assiut, Egypt | ||||
| Abstract | ||||
| We study the problem of underwater debris classification and removal by remotely operated vehicles. This task is particularly important for subsea oil and gas fields exploitation. The classification of underwater debris is a challenging and difficult problem because of the complexity of underwater environments. We investigate four different algorithms based on deep convolutional neural networks for detecting and classifying marine debris. The proposed techniques are built on Keras and Tensorflow using Python programming environment. To train the algorithm for detection, various dataset information containing different types of marine debris have been established. Four distinct classifier and activation function combinations have been compared experimentally. The dataset is consist of fifteen category. The suggested approach is a modified VGGNet-16 trained on the dataset. The use of a sigmoid classifier and the Relu activation function to categories marine improves classification accuracy. The overall result indicates that classification accuracy on the testing set is satisfactory. | ||||
| Keywords | ||||
| Keywords— Remotely operated vehicles; Marine debris; Deep learning; Convolutional neural network; Image processing | ||||
| References | ||||
|
[1] F.C.Soon,H.Y.Khaw,J.H.Chuah,J.Kanesan,Hyper parameters optimization of deep CNN architecture for vehicle logo recognition, IET Intelligent Transport Systems 12 (8) (2018) 939–946.
[2] Sinha, H., Awasthi, V., Ajmera, P. K.: Audio classification using braided convolutional neural networks. IET Signal Processing 14(7), 448–454 (2020).
[3] Umar Albalawi, S Manimurugan and R Varatharajan, "Classification of breast cancer mammogram images using convolution neural network", Concurrency and Computation: Practice and Experience, no. 1, pp. 1-12, 2020.
[4] Ferraz, P.A.P.; de Oliveira, B.A.G.; Ferreira, F.M.F.; Martins, C.A.P.d.S. Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intell. Transp. Syst. 2020, 14, 1319–1327.
[5] Shen, C.; Zhao, X.; Fan, X.; Lian, X.; Zhang, F.; Kreidieh, A.R.; Liu, Z. Multi-receptive field graph convolutional neural networks for pedestrian detection. IET Intell. Trans. Syst. 2019, 13, 1319–1328.
[6] Wang Q, Liao J, Lapata M, Macleod M. Risk of bias assessment in preclinical literature using natural language processing. Res Synth Methods. 2021 Oct 28
[7] Salman, A., Jalal, A., Shafait, F., Mian, A., Shortis, M., Seager, J., Harvey, E., 2016. Fish species classification in unconstrained underwater environments based on deep learning. Limnol. Oceanogr. Methods 14 (9), 570–585.
[8] Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
[9] Laila Ma‟rifatul Azizah, Sitti Fadillah Umayah, Slamet Riyadi, Cahya Damarjati, Nafi Ananda Utama “Deep Learning Implementation using Convolutional Neural Network in Mangosteen Surface Defect Detection”, ICCSCE, ISBN 978-1-5386-3898-9, pp. 242-246, 2017.
[10] Guidelines for the selection and Operation of Jack-ups in the Marine Renewable Energy Industry issue 2 :2013.
[11] G. Clauss, E. Lehmann, C. ÖstergaardOffshore structures, volume I, conceptual design and hydrodynamicsSpringer-Verlag, New York (1992).
[12] Mohamed Elawady. Sparse coral classification using deep convolutional neural networks. arXiv preprint arXiv:1511.09067, 2015.
[13] Ammar Mahmood, Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid, Renae Hovey, Gary Kendrick, and Robert B Fisher. Coral classification with hybrid feature representations. In 2016 IEEE In- ternational Conference on Image Processing (ICIP), pages 519–523. IEEE, 2016.
[14] Hansang Lee, Minseok Park, and Junmo Kim. Plank- ton classification on imbalanced large-scale database via convolutional neural networks with transfer learn- ing. In 2016 IEEE international conference on image processing (ICIP), pages 3713–3717. IEEE, 2016.
[15] Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 25, 1097–1105.
[16] Nicole Seese, Andrew Myers, Kaleb Smith, and An- thony O Smith. Adaptive foreground extraction for deep fish classification. In 2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI), pages 19–24. IEEE, 2016.
[17] Yong Bai, Qiang Bai, in Subsea Engineering Handbook (Second Edition), 2019, pages 320-340.
[18] Dipta Gomes , A.F.M. Saifuddin Saif . Robust Underwater Fish Detection Using an Enhanced Convolutional Neural Network. June 2021International Journal of Image, Graphics and Signal Processing 13(3):44-54 | ||||
|
Statistics Article View: 139 PDF Download: 222 |
||||
