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Target Classification of Marine Debris Using Deep Learning

Anum Aleem1, Samabia Tehsin1,*, Sumaira Kausar1, Amina Jameel2

1 Bahria University, Islamabad, Pakistan
2 Bahria University, Karachi, Pakistan

* Corresponding Author: Samabia Tehsin. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 73-85.


Marine Debris is human-created waste dumped into the sea or ocean. It pollutes the aquatic environment and hence very dangerous for ocean species. Removal of marine debris from ocean is necessary to eliminate pollution and to secure aquatic life. A robust and automatic system is essential that detects unnecessary litter of plastic and other garbage at real-time. In this study, we have proposed deep learning based architecture for the detection and classification of marine debris. Histogram Equalization technique combined with Median Filter is used to enhance the contrast of images and to remove noise. Experiments are performed on challenging Forward Looking Sonar Image (FLS) Marine Debris Dataset. This dataset includes ten different types of Debris. The proposed system not only detect the Debris, but also classify it into ten classes. To overcome the challenge of data scarcity, Faster-RCNN with transfer learning of ResNet-50 architecture is used. Faster-RCNN is one of the popular object detection architecture that uses Regional Proposal Network (RPN) and detector at the same time. The proposed methodology significantly improves the state-of-the-art results. Result assessment of our proposed technique achieved recall of (96%) and Mean Overlap bounding boxes of (3.78). Visual and qualitative assessment of proposed methodology shows the effectiveness of presented technique.


Cite This Article

A. Aleem, S. Tehsin, S. Kausar and A. Jameel, "Target classification of marine debris using deep learning," Intelligent Automation & Soft Computing, vol. 32, no.1, pp. 73–85, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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