@article{ref1,
title="Underwater human detection using faster R-CNN with data augmentation",
journal="Materials today: proceedings",
year="2023",
author="Dulhare, U.N. and Hussam Ali, M.",
volume="80",
number="",
pages="1940-1945",
abstract="According to WHO, Drowning is a very much serious and every year 3,74,000 people claiming the lives as a public health threat. Underwater human body detection through live video is a very challenging task because of movement of water currents continuously and changes the intensity of water. Detecting people from an underwater video is a very complex and challenging problem as the video can be affected by various factors such as undesired artifacts (e.g. noise), monitoring limitation of cameras, illumination variation etc. It is very important to build the appropriate model to address this problem. The major goal of this paper is to examine the possibility of detect humans in an underwater Ambient using one of the Faster R-Convolution neural network (Faster R-CNN) and data enhancement algorithms and acutely, recall and precise evaluation of the proposed model results. <p /> <p>Language: en</p>",
language="en",
issn="2214-7853",
doi="10.1016/j.matpr.2021.05.653",
url="http://dx.doi.org/10.1016/j.matpr.2021.05.653"
}