Robust Underwater Animal Detection Adopting CNN with LSTM

摘要

Underwater detection of objects valuable problem for many civil and military applications such as hydrographic surveys for the purpose of ensuring navigation. The objective of this work is to ensure flexibility, speed, and precise recognition of object underwater system for use in a variety of low-level underwater images captured. These imaging systems are used in separate occasions and under distinct weather and bathymetric circumstances from underwater imaging systems. This paper discusses the use of deep learning in the latest past to analyze underwater imagery. The methods to analyze are classified according to the object of detection, highlighting the characteristics and architectures used for deep learning. In the evaluation of digital sea bed imagery using deep neural networks, it is found that there is excellent scope for automation, particularly for the detection and tracking of detected object is harmful or non-harmful.