A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="http://www.mecs-press.org/ijigsp/ijigsp-v13-n3/IJIGSP-V13-N3-4.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="MECS Publisher">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7hgv6dkr7vaq7mvfy3joc3nu2y" style="color: black;">International Journal of Image Graphics and Signal Processing</a>
Underwater Object Detection is one of the most challenging and unexplored domains in this area of Computer Vision. The proposed research refines the image enhancement of under-water imagery by proposing an improvement of already existing tools for underwater Object detection. The comparative study clearly depicts the enhancement of the proposed method with respect to the existing methods for underwater object detection. Moreover, a framework for detection of underwater organisms such as fishes<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijigsp.2021.03.04">doi:10.5815/ijigsp.2021.03.04</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sa6bbpulufevfazhl2hqzvwu5q">fatcat:sa6bbpulufevfazhl2hqzvwu5q</a> </span>
more »... re proposed, which will act as the steppingstone for reserving the ecosystem of the whole fish community. Mostly the object detection using deep learning has been the prime goal to this research and the comparison between other preexisting methods are compared at the end. As a result, techniques that are already well established will be used for overall enhancement of those images. Through this enhancement and through finding a healthy environment for their breeding ground, the extinction of selected species of fishes is can be diminished and decreased. All this is carried out by overcoming difficulties underwater through a novel technique that can be integrated into an Underwater Autonomous Vehicle and can be classified as robust in nature. Robustness will depend on three important factors in this research, first is accuracy, then fast and lastly being upgradeable. The proposed method is a modified VGGNet-16, which is trained using the ImageCLEF FISH_TS dataset. The overall result provides an accuracy of 96.4% which surpasses all its predecessors.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210716105447/http://www.mecs-press.org/ijigsp/ijigsp-v13-n3/IJIGSP-V13-N3-4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/71/8f/718f88366b8a9a547d9f5b88e002f631e4a8c4e6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijigsp.2021.03.04"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>