YOLOv8在水下垃圾检测中表现最佳，准确率高达80.9%。
Underwater Waste Detection Using Deep Learning A Performance Comparison of YOLOv7 to 10 and Faster RCNN
- 对比YOLOv7至v10和Faster R-CNN，YOLOv8采用无锚框机制与自监督学习
- 在复杂水下环境下，平均精度（mAP）达到80.9%，优于其他模型
- 适合用于海洋污染监测与自动化清理系统开发
水下污染是当今最严峻的环境问题之一，全球海河湖中发现大量垃圾。准确识别这些废弃物对环境监测与治理至关重要。本研究评估了五种先进目标检测算法在水下场景中的表现，包括YOLOv7、YOLOv8、YOLOv9、YOLOv10及Faster R-CNN。模型在包含十五类垃圾的大型数据集上训练与测试，覆盖低能见度、不同水深等多种复杂条件。结果显示，YOLOv8表现最优，平均精度（mAP）达80.9%。其性能提升归因于改进的无锚框机制与自监督学习结构，使模型在多样环境中实现更精准高效的识别。该成果表明，YOLOv8可作为应对全球污染的重要工具，显著提升水下清理作业的检测能力与可扩展性。
原文摘要 · Abstract (English)
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.
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