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Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network

Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network

来源:Arxiv_logoArxiv
英文摘要

Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current monitoring technologies for detecting underwater litter face limitations in survey efficiency, cost, and environmental conditions, highlighting the need for efficient, consumer-grade technologies for automatic detection. This research introduces the Aerial-Aquatic Speedy Scanner (AASS) combined with Super-Resolution Reconstruction (SRR) and an improved YOLOv8 detection network. AASS enhances data acquisition efficiency over traditional methods, capturing high-quality images that accurately identify underwater waste. SRR improves image-resolution by mitigating motion blur and insufficient resolution, thereby enhancing detection tasks. Specifically, the RCAN model achieved the highest mean average precision (mAP) of 78.6% for detection accuracy on reconstructed images among the tested SRR models. With a magnification factor of 4, the SRR test set shows an improved mAP compared to the conventional bicubic set. These results demonstrate the effectiveness of the proposed method in detecting underwater litter.

Fan Zhao、Yongying Liu、Yijia Chen、Jiaqi Wang、Xinlei Shao、Dianhan Xi、Shigeru Tabeta、Katsunori Mizuno

10.1016/j.marpolbul.2024.117030

环境科学技术现状环境污染、环境污染防治废物处理、废物综合利用

Fan Zhao,Yongying Liu,Yijia Chen,Jiaqi Wang,Xinlei Shao,Dianhan Xi,Shigeru Tabeta,Katsunori Mizuno.Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network[EB/OL].(2025-07-08)[2025-07-23].https://arxiv.org/abs/2408.03564.点此复制

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