5 papers
PLAS-Net: Pixel-Level Area Segmentation for UAV-Based Beach Litter Monitoring
Yongying Liu, Jiaqi Wang, Jian Song +6
Accurate quantification of the physical exposure area of beach litter, rather than simple item counts, is essential for credible ecological risk assessment of marine debris. Howeve…
Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning
Xinlei Shao, Hongruixuan Chen, Fan Zhao +5
Coral reef ecosystems provide essential ecosystem services, but face significant threats from climate change and human activities. Although advances in deep learning have enabled a…
Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network
Fan Zhao, Yongying Liu, Jiaqi Wang +5
Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current monitoring technologies for de…
Enhanced hermit crabs detection using super-resolution reconstruction and improved YOLOv8 on UAV-captured imagery
Fan Zhao, Yijia Chen, Dianhan Xi +4
Hermit crabs play a crucial role in coastal ecosystems by dispersing seeds, cleaning up debris, and disturbing soil. They serve as vital indicators of marine environmental health,…
Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry
Xinlei Shao, Hongruixuan Chen, Kirsty Magson +4
Since coral reef ecosystems face threats from human activities and climate change, coral conservation programs are implemented worldwide. Monitoring coral health provides reference…