Research Challenges, Recent Advances and Benchmark Datasets in
Deep-Learning-Based Underwater Marine Object Detection: A Review
- Meng Joo Er ,
- Chen Jie ,
- Yani Zhang ,
- Wenxiao Gao
Chen Jie
Institute of Artificial Intelligence and Marine Robotics, Institute of Artificial Intelligence and Marine Robotics, Dalian Maritime University, Dalian Maritime University
Corresponding Author:[email protected]
Author ProfileAbstract
Underwater marine object detection, as one of the most fundamental
techniques in marine engineering, has been shown to exhibit significant
potential for exploring underwater environment in recent years. It has
been applied widely in the monitoring of underwater ecosystems,
exploration of natural resources, management of commercial fishery
management, and other areas. However, due to the complexity of
underwater environment, characteristics of marine object, and limitation
from exploration equipment, the detection performance revolving around
speed, accuracy and robustness could be degraded dramatically. In this
context, we present a survey of deep-learning-based underwater marine
object detection. To facilitate comprehensive understanding of the
subject matter, we categorize the existing research challenges of
underwater object detection into image quality degradation, small object
detection, poor generalization and real-time detection. Corresponding to
each category of the existing challenges, we review recent advances and
highlight the pros and cons of existing techniques. Furthermore, we give
a detailed and critical review of the most widely used benchmark
datasets for underwater marine object detection. Comparisons with
existing reviews and future trends of the subject matter, particularly
AI-based techniques, are also presented.