Matches in SemOpenAlex for { <https://semopenalex.org/work/W3207336119> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W3207336119 endingPage "5267" @default.
- W3207336119 startingPage "5251" @default.
- W3207336119 abstract "Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an image restoration phase, pre-processing based on diffraction correction is primarily applied to frames. The YOLOv3 based object recognition system is used to identify fish occurrences. The objects in the background that are camouflaged are often overlooked by the YOLOv3 model. A proposed Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm, adapted by Gaussian mixture models, and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection method. The proposed approach was tested on four challenging video datasets, the Life Cross Language Evaluation Forum (CLEF) benchmark from the F4K data repository, the University of Western Australia (UWA) dataset, the bubble vision dataset and the DeepFish dataset. The accuracy for fish identification is 98.5 percent, 96.77 percent, 97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method." @default.
- W3207336119 created "2021-10-25" @default.
- W3207336119 creator A5006457038 @default.
- W3207336119 creator A5023804505 @default.
- W3207336119 creator A5050698107 @default.
- W3207336119 creator A5057872023 @default.
- W3207336119 date "2022-01-01" @default.
- W3207336119 modified "2023-10-14" @default.
- W3207336119 title "Deep Neural Network Driven Automated Underwater Object Detection" @default.
- W3207336119 cites W1976263166 @default.
- W3207336119 cites W2002198246 @default.
- W3207336119 cites W2077896037 @default.
- W3207336119 cites W2091420866 @default.
- W3207336119 cites W2135001643 @default.
- W3207336119 cites W2523532944 @default.
- W3207336119 cites W2587107113 @default.
- W3207336119 cites W2771659028 @default.
- W3207336119 cites W2792915118 @default.
- W3207336119 cites W284446012 @default.
- W3207336119 cites W2889983693 @default.
- W3207336119 cites W2899115934 @default.
- W3207336119 cites W2949744249 @default.
- W3207336119 cites W2997344004 @default.
- W3207336119 cites W3014286955 @default.
- W3207336119 doi "https://doi.org/10.32604/cmc.2022.021168" @default.
- W3207336119 hasPublicationYear "2022" @default.
- W3207336119 type Work @default.
- W3207336119 sameAs 3207336119 @default.
- W3207336119 citedByCount "4" @default.
- W3207336119 countsByYear W32073361192022 @default.
- W3207336119 crossrefType "journal-article" @default.
- W3207336119 hasAuthorship W3207336119A5006457038 @default.
- W3207336119 hasAuthorship W3207336119A5023804505 @default.
- W3207336119 hasAuthorship W3207336119A5050698107 @default.
- W3207336119 hasAuthorship W3207336119A5057872023 @default.
- W3207336119 hasBestOaLocation W32073361191 @default.
- W3207336119 hasConcept C111368507 @default.
- W3207336119 hasConcept C127313418 @default.
- W3207336119 hasConcept C13280743 @default.
- W3207336119 hasConcept C153180895 @default.
- W3207336119 hasConcept C154945302 @default.
- W3207336119 hasConcept C185798385 @default.
- W3207336119 hasConcept C197129107 @default.
- W3207336119 hasConcept C205649164 @default.
- W3207336119 hasConcept C23123220 @default.
- W3207336119 hasConcept C2776151529 @default.
- W3207336119 hasConcept C31972630 @default.
- W3207336119 hasConcept C41008148 @default.
- W3207336119 hasConcept C61224824 @default.
- W3207336119 hasConcept C98083399 @default.
- W3207336119 hasConceptScore W3207336119C111368507 @default.
- W3207336119 hasConceptScore W3207336119C127313418 @default.
- W3207336119 hasConceptScore W3207336119C13280743 @default.
- W3207336119 hasConceptScore W3207336119C153180895 @default.
- W3207336119 hasConceptScore W3207336119C154945302 @default.
- W3207336119 hasConceptScore W3207336119C185798385 @default.
- W3207336119 hasConceptScore W3207336119C197129107 @default.
- W3207336119 hasConceptScore W3207336119C205649164 @default.
- W3207336119 hasConceptScore W3207336119C23123220 @default.
- W3207336119 hasConceptScore W3207336119C2776151529 @default.
- W3207336119 hasConceptScore W3207336119C31972630 @default.
- W3207336119 hasConceptScore W3207336119C41008148 @default.
- W3207336119 hasConceptScore W3207336119C61224824 @default.
- W3207336119 hasConceptScore W3207336119C98083399 @default.
- W3207336119 hasIssue "3" @default.
- W3207336119 hasLocation W32073361191 @default.
- W3207336119 hasOpenAccess W3207336119 @default.
- W3207336119 hasPrimaryLocation W32073361191 @default.
- W3207336119 hasRelatedWork W1983333094 @default.
- W3207336119 hasRelatedWork W2309573947 @default.
- W3207336119 hasRelatedWork W2742702720 @default.
- W3207336119 hasRelatedWork W2754428891 @default.
- W3207336119 hasRelatedWork W2782964878 @default.
- W3207336119 hasRelatedWork W2787282005 @default.
- W3207336119 hasRelatedWork W2922421953 @default.
- W3207336119 hasRelatedWork W3002270006 @default.
- W3207336119 hasRelatedWork W3080115630 @default.
- W3207336119 hasRelatedWork W4281560450 @default.
- W3207336119 hasVolume "70" @default.
- W3207336119 isParatext "false" @default.
- W3207336119 isRetracted "false" @default.
- W3207336119 magId "3207336119" @default.
- W3207336119 workType "article" @default.