A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions

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Abstract

Underwater pollution, particularly from plastic and other debris, poses a serious environmental threat to marine ecosystems and biodiversity. Traditional methods for detecting underwater trash, such as sonar-based systems and manual inspections, face significant limitations, especially in deep, turbid waters with low visibility. Recently, deep learning algorithms, including Convolutional Neural Networks (CNNs) and frameworks like YOLO (You Only Look Once) and Faster R-CNN, have shown promising results in automating underwater trash detection. These models, trained on large datasets like Trash Can, offer high accuracy and real-time detection capabilities. However, challenges persist, such as environmental variability, including changes in water clarity, light conditions, and surface disturbances, which can distort images and reduce detection accuracy. Additionally, the lack of comprehensive, annotated datasets, particularly for small debris like microplastics, and issues related to data imbalance complicate the development of robust detection systems. Despite these obstacles, deep learning models continue to improve with advancements in model architectures, data augmentation techniques, and integration of multimodal sensor data, such as sonar, to enhance detection in varied underwater conditions. The future of underwater trash detection lies in overcoming these challenges by optimizing lightweight, real-time models for resource-constrained platforms and enhancing detection of small and overlapping debris. This paper provides a comprehensive review of current deep learning techniques for underwater trash detection, highlighting advancements, challenges, and future research directions for improving model performance and scalability.

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Keywords

Underwater trash detection, deep learning, YOLO, Faster R-CNN, CNN, microplastics, environmental variability, data augmentation, real-time detection, marine pollution.