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Underwater object detection: architectures and algorithms – a comprehensive review

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Abstract

Underwater object detection is an essential step in image processing and it plays a vital role in several applications such as the repair and maintenance of sub-aquatic structures and marine sciences. Many computer vision-based solutions have been proposed but an optimal solution for underwater object detection and species classification does not exist. This is mainly because of the challenges presented by the underwater environment which mainly include light scattering and light absorption. The advent of deep learning has enabled researchers to solve various problems like protection of the subaquatic ecological environment, emergency rescue, reducing chances of underwater disaster and its prevention, underwater target detection, spooring, and recognition. However, the advantages and shortcomings of these deep learning algorithms are still unclear. Thus, to give a clearer view of the underwater object detection algorithms and their pros and cons, we proffer a state-of-the-art review of different computer vision-based approaches that have been developed as yet. Besides, a comparison of various state-of-the-art schemes is made based on various objective indices and future research directions in the field of underwater object detection have also been proffered.

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Authors and Affiliations

  1. Department of Electronics and Inst. Technology, University of Kashmir, Hazratbal, Srinagar, India

    Sheezan Fayaz & Shabir A. Parah

  2. Higher Education Department, Government of J and K, Srinagar, India

    G. J. Qureshi

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  1. Sheezan Fayaz
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  2. Shabir A. Parah
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  3. G. J. Qureshi
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Fayaz, S., Parah, S.A. & Qureshi, G.J. Underwater object detection: architectures and algorithms – a comprehensive review. Multimed Tools Appl 81, 20871–20916 (2022). https://doi.org/10.1007/s11042-022-12502-1

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