FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3209526300> ?p ?o ?g. }

• W3209526300 endingPage "102215" @default.
• W3209526300 startingPage "102215" @default.
• W3209526300 abstract "Fish counting is of great significance for aquaculture companies to formulate feeding strategies and management plans. Preceding computer vision methods are difficult to solve the counting problem when the fish body is seriously overlapped and the shape changes greatly. In this research, a deep learning network model based on multi-modules and attention mechanism (MAN) is proposed to realize the counting of cultured fish which consists of feature extraction module, attention module, and density map estimation module. Among them, the feature extraction module is composed of three parallel convolutional networks which is used to extract the general feature map of the image and serves as the input of the subsequent module. The trunk convolutional network and the attention network are connected in parallel to form the attention module which is more precisely identify critical information of image in dense counting process. Thereinto, the attention network integrated a residual block without batch normalization and a convolutional layer. The distribution and the number of fishes in the image is represented by density map estimation module. In order to improve the counting accuracy and retain more details of the image, the sum of squared error (SSE) and structural similarity index (SSIM) are applied to form the loss function and train the model. In order to explore the generalization ability of the model, data from two tanks were used to validate the model. The experimental results for MAN showed that the counting accuracy is about 97.12%, and the deviation is 3.67. Compared with the MCNN and CNNs, the accuracy of MAN has been improved by about 1.95% and 2.76%. At the same time, the deviation of MAN has also been reduced by 30.23% and 41.09%, respectively. For the images of another tank, the counting accuracy of MAN is 94.69%, while the MCNN and CNNs cannot meet the application requirements of fish counting. Furthermore, the accuracy for MAN between the different distribution intervals of the number of fishes is fluctuating less. On the whole, the model not only has high counting accuracy, but also has strong stability and generalization performance. The method that based on multi-modules and attention mechanism can promote the exploration of the technology related to dense fish counting, and provide a feasible solution for the application of this technology in practical engineering." @default.
• W3209526300 created "2021-11-08" @default.
• W3209526300 creator A5000446896 @default.
• W3209526300 creator A5011791047 @default.
• W3209526300 creator A5018841457 @default.
• W3209526300 creator A5066463037 @default.
• W3209526300 date "2022-02-01" @default.
• W3209526300 modified "2023-10-15" @default.
• W3209526300 title "Counting method for cultured fishes based on multi-modules and attention mechanism" @default.
• W3209526300 cites W1965285355 @default.
• W3209526300 cites W2039511464 @default.
• W3209526300 cites W2048201075 @default.
• W3209526300 cites W2063422727 @default.
• W3209526300 cites W2070723007 @default.
• W3209526300 cites W2072739957 @default.
• W3209526300 cites W2073797363 @default.
• W3209526300 cites W2087942585 @default.
• W3209526300 cites W2147675406 @default.
• W3209526300 cites W2150683269 @default.
• W3209526300 cites W2194775991 @default.
• W3209526300 cites W2288990755 @default.
• W3209526300 cites W2463631526 @default.
• W3209526300 cites W2790970743 @default.
• W3209526300 cites W2883363148 @default.
• W3209526300 cites W2904868325 @default.
• W3209526300 cites W2941123315 @default.
• W3209526300 cites W2944299331 @default.
• W3209526300 cites W2963495494 @default.
• W3209526300 cites W2963681621 @default.
• W3209526300 cites W2963893037 @default.
• W3209526300 cites W2969522893 @default.
• W3209526300 cites W2977808367 @default.
• W3209526300 cites W2982993655 @default.
• W3209526300 cites W2986670051 @default.
• W3209526300 cites W2997616577 @default.
• W3209526300 cites W2998543752 @default.
• W3209526300 cites W3003025382 @default.
• W3209526300 cites W3011408087 @default.
• W3209526300 cites W3033927182 @default.
• W3209526300 cites W3094321741 @default.
• W3209526300 cites W3097561888 @default.
• W3209526300 doi "https://doi.org/10.1016/j.aquaeng.2021.102215" @default.
• W3209526300 hasPublicationYear "2022" @default.
• W3209526300 type Work @default.
• W3209526300 sameAs 3209526300 @default.
• W3209526300 citedByCount "11" @default.
• W3209526300 countsByYear W32095263002022 @default.
• W3209526300 countsByYear W32095263002023 @default.
• W3209526300 crossrefType "journal-article" @default.
• W3209526300 hasAuthorship W3209526300A5000446896 @default.
• W3209526300 hasAuthorship W3209526300A5011791047 @default.
• W3209526300 hasAuthorship W3209526300A5018841457 @default.
• W3209526300 hasAuthorship W3209526300A5066463037 @default.
• W3209526300 hasConcept C134306372 @default.
• W3209526300 hasConcept C136886441 @default.
• W3209526300 hasConcept C138885662 @default.
• W3209526300 hasConcept C144024400 @default.
• W3209526300 hasConcept C153180895 @default.
• W3209526300 hasConcept C154945302 @default.
• W3209526300 hasConcept C177148314 @default.
• W3209526300 hasConcept C19165224 @default.
• W3209526300 hasConcept C2776401178 @default.
• W3209526300 hasConcept C33923547 @default.
• W3209526300 hasConcept C41008148 @default.
• W3209526300 hasConcept C41895202 @default.
• W3209526300 hasConcept C52622490 @default.
• W3209526300 hasConcept C81363708 @default.
• W3209526300 hasConceptScore W3209526300C134306372 @default.
• W3209526300 hasConceptScore W3209526300C136886441 @default.
• W3209526300 hasConceptScore W3209526300C138885662 @default.
• W3209526300 hasConceptScore W3209526300C144024400 @default.
• W3209526300 hasConceptScore W3209526300C153180895 @default.
• W3209526300 hasConceptScore W3209526300C154945302 @default.
• W3209526300 hasConceptScore W3209526300C177148314 @default.
• W3209526300 hasConceptScore W3209526300C19165224 @default.
• W3209526300 hasConceptScore W3209526300C2776401178 @default.
• W3209526300 hasConceptScore W3209526300C33923547 @default.
• W3209526300 hasConceptScore W3209526300C41008148 @default.
• W3209526300 hasConceptScore W3209526300C41895202 @default.
• W3209526300 hasConceptScore W3209526300C52622490 @default.
• W3209526300 hasConceptScore W3209526300C81363708 @default.
• W3209526300 hasFunder F4320321540 @default.
• W3209526300 hasFunder F4320335777 @default.
• W3209526300 hasLocation W32095263001 @default.
• W3209526300 hasOpenAccess W3209526300 @default.
• W3209526300 hasPrimaryLocation W32095263001 @default.
• W3209526300 hasRelatedWork W2359348847 @default.
• W3209526300 hasRelatedWork W2591697403 @default.
• W3209526300 hasRelatedWork W2904022177 @default.
• W3209526300 hasRelatedWork W2904857019 @default.
• W3209526300 hasRelatedWork W2944728705 @default.
• W3209526300 hasRelatedWork W2953716828 @default.
• W3209526300 hasRelatedWork W3011538607 @default.
• W3209526300 hasRelatedWork W4294432981 @default.
• W3209526300 hasRelatedWork W4321276295 @default.
• W3209526300 hasRelatedWork W4321441197 @default.
• W3209526300 hasVolume "96" @default.
• W3209526300 isParatext "false" @default.

• next