Research Challenges, Recent Advances, and Popular Datasets in Deep Learning-Based Underwater Marine Object Detection:...

Area of Science:

• Marine Science and Engineering
• Computer Vision
• Artificial Intelligence

Background:

• Underwater marine object detection is crucial for ocean exploration, ecosystem monitoring, and resource management.
• Conventional methods struggle with underwater environmental complexities, affecting speed, accuracy, and robustness.
• Deep learning offers significant potential to overcome these limitations in marine applications.

Purpose of the Study:

• To review deep learning-based techniques for vision-based underwater marine object detection.
• To categorize and analyze key research challenges: image quality, small object detection, generalization, and real-time performance.
• To provide a critical examination of datasets and comparative studies.

Main Methods:

• Focus on vision-based object detection methods, reviewing recent advances.
• Organize challenges into four distinct categories for structured analysis.
• Critically examine existing datasets and compare different deep learning approaches.

Main Results:

• Identified key challenges in underwater object detection: image degradation, small object detection, poor generalization, and real-time processing.
• Reviewed and highlighted the advantages and disadvantages of current deep learning solutions for each challenge.
• Provided a detailed examination of widely used datasets.

Conclusions:

• Deep learning techniques show significant promise for advancing underwater marine object detection.
• Addressing specific challenges like image quality and small object detection is critical for practical applications.
• Future research should focus on improving generalization and real-time capabilities for robust ocean exploration.