Item Infomation


Title: 
Context-Driven Detection of Invertebrate Species in Deep-Sea Video
Authors: 
R. Austin, McEver
Bowen, Zhang
Connor, Levenson
Issue Date: 
2023
Publisher: 
Springer
Abstract: 
Each year, underwater remotely operated vehicles (ROVs) collect thousands of hours of video of unexplored ocean habitats revealing a plethora of information regarding biodiversity on Earth. However, fully utilizing this information remains a challenge as proper annotations and analysis require trained scientists’ time, which is both limited and costly. To this end, we present a Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), a benchmark suite and growing large-scale dataset to train, validate, and test methods for temporally localizing four underwater substrates as well as temporally and spatially localizing 59 underwater invertebrate species.
Description: 
CC BY
URI: 
https://link.springer.com/article/10.1007/s11263-023-01755-4
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8236
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