Discovering unknowns: Context-enhanced anomaly detection for curiosity-driven autonomous underwater exploration

Discovering unknown objects from visual information as curiosity is highly demanded for autonomous exploration in underwater environment. In this research, we propose an end-to-end deep neural network for anomaly detection in the highly dynamic unstructured underwater background faced by a moving ro...

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Bibliographic Details

Main Authors:	Yang Zhou, Baihua Li, Jiangtao Wang, Emanuele Rocco, Qinggang Meng
Format:	Default Article
Published:	2022
Subjects:	Artificial intelligence not elsewhere classified Information systems not elsewhere classified Anomaly detection Learning unknown objects Deep learning autoencoder Autonomous underwater robotics Information Systems Artificial Intelligence and Image Processing
Online Access:	https://hdl.handle.net/2134/20141432.v1
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