Underwater pollution is one of today's most significant environmental concerns, with vast volumes of garbage found in seas, rivers, and landscapes around the world. Accurate detection of these waste materials is crucial for successful waste management, environmental monitoring, and mitigation strategies. In this study, we investigated the performance of five cutting-edge object recognition algorithms, namely YOLO (You Only Look Once) models, including YOLOv7, YOLOv8, YOLOv9, YOLOv10, and Faster Region-Convolutional Neural Network (R-CNN), to identify which model was most effective at recognizing materials in underwater situations. The models were thoroughly trained and tested on a large dataset containing fifteen different classes under diverse conditions, such as low visibility and variable depths. From the above-mentioned models, YOLOv8 outperformed the others, with a mean Average Precision (mAP) of 80.9%, indicating a significant performance. This increased performance is attributed to YOLOv8's architecture, which incorporates advanced features such as improved anchor-free mechanisms and self-supervised learning, allowing for more precise and efficient recognition of items in a variety of settings. These findings highlight the YOLOv8 model's potential as an effective tool in the global fight against pollution, improving both the detection capabilities and scalability of underwater cleanup operations.

翻译：水下污染是当今最严峻的环境问题之一，全球海洋、河流及水域环境中存在大量垃圾。准确检测这些废弃物对于有效的废物管理、环境监测和治理策略至关重要。本研究评估了五种前沿目标识别算法在水下场景中的性能，包括YOLO（You Only Look Once）系列的YOLOv7、YOLOv8、YOLOv9、YOLOv10以及Faster Region-Convolutional Neural Network（R-CNN），旨在确定何种模型在水下物质识别中最为有效。这些模型在包含15个类别、涵盖低能见度和多深度等复杂条件的大规模数据集上进行了全面训练与测试。实验结果表明，YOLOv8以80.9%的平均精度均值（mAP）显著优于其他模型。其卓越性能归因于架构创新，包括改进的无锚框机制和自监督学习等先进特性，使其能在多样化环境中实现更精准高效的识别。这些发现凸显了YOLOv8模型作为全球污染治理有效工具的潜力，可显著提升水下清洁作业的检测能力与可扩展性。