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Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

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posted on 24.03.2022, 21:40 authored by Meng Joo Er, Chen JieChen Jie
Underwater marine object detection, as one of the most fundamental techniques in marine engineering, had been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been applied widely in monitoring of underwater ecosystems, exploration of natural resources, management of commercial fishery, and so on. However, due to the complexity of the underwater environment, characteristics of marine objects, and limitations from exploration equipment, the detection performance in terms of speed, accuracy and robustness could be degraded dramatically using conventional approaches. Deep learning algorithms have found significant impact in many fields of engineering and science including marine engineering. In this context, We offer an overview of deep-learning-based underwater marine object detection techniques. We organize the present research challenges into three areas to assist a thorough knowledge of the subject matter, namely image quality degradation, small object detection, and poor generalization. We evaluate recent advances and emphasize the advantages and risks of existing solutions for each category of extant challenges. In addition, we examine the most extensively used benchmark datasets for underwater marine object detection in detail and critically. Comparisons between previous reviews and prospective advancements in the subject field, notably Artificial-Intelligence-based approaches, are also presented.

Funding

3132019344

00253007

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Email Address of Submitting Author

gxuchenjie04@gmail.com

Submitting Author's Institution

Dalian Maritime University

Submitting Author's Country

China

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