Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future

TL;DR

This paper provides a comprehensive survey of AI-based underwater object detection, analyzing current methods, challenges, and future directions, along with evaluations and tools to improve detector performance.

Contribution

It categorizes existing algorithms, evaluates them across datasets, and introduces analysis tools, offering a thorough overview and insights for future research in underwater object detection.

Findings

Deep learning methods outperform traditional approaches in UOD.

Benchmark evaluations reveal dataset biases affecting performance.

Analysis tools diagnose detector strengths and weaknesses.

Abstract

Underwater object detection (UOD), aiming to identify and localise the objects in underwater images or videos, presents significant challenges due to the optical distortion, water turbidity, and changing illumination in underwater scenes. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD. To further facilitate future advancements, we comprehensively study AI-based UOD. In this survey, we first categorise existing algorithms into traditional machine learning-based methods and deep learning-based methods, and summarise them by considering learning strategy, experimental dataset, utilised features or frameworks, and learning stage. Next, we discuss the potential challenges and suggest possible solutions and new directions. We also perform both quantitative and qualitative evaluations of mainstream algorithms across multiple benchmark datasets by considering the diverse and biased experimental setups. Finally, we introduce two off-the-shelf detection analysis tools, Diagnosis and TIDE, which well-examine the effects of object characteristics and various types of errors on detectors. These tools help identify the strengths and weaknesses of detectors, providing insigts for further improvement. The source codes, trained models, utilised datasets, detection results, and detection analysis tools are public available at \url{https://github.com/LongChenCV/UODReview}, and will be regularly updated.