A Deep Learning Approach to Detecting Objects in Underwater Images

Abstract

A deep learning approach, also known as deep machine learning or deep structure learning, has recently been found to be successful in categorizing digital images and detecting objects within them. Consequently, it has rapidly gained attention and a reputation in computer vision research. Aquatic ecosystems, especially seagrass beds, are increasingly observed using digital photographs. Automatic detection and classification now requires deep neural network-based classifiers due to the increase in image data. The purpose of this paper is to present a systematic method for analyzing recent underwater pipeline imagery using deep learning. There is a logical organization of the analytical methods based on the recognized items, as well as an outline of the deep learning architectures employed. Deep neural network analysis of digital photographs of the seafloor has a lot of potential for automation, particularly in the discovery and monitoring of underwater pipeline images.