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- W4366380935 abstract "At present, most of the work of using images to identify Sousa Chinensis still relies on manual labor, which is time-consuming and difficult to process a large number of pictures taken during the survey in time. This paper proposes an image recognition method of Sousa Chinensis based on deep learning to improve Sousa Chinensis' recognition efficiency and accuracy significantly. Using technologies such as the improved YOLO V5 neural network model and the Kmeans clustering algorithm, Sousa Chinensis ' images can be quickly and accurately recognized. The dorsal fin area can be automatically selected to add keyword labels. After the successful identification of the dorsal fin profile of the Sousa Chinensis, this study also accurately distinguished the Sousa Chinensis among individuals by constructing a convolutional neural network, ResNet101 network model, and Faster-RCNN target detection algorithm according to the occurrence time, gray level of body color, dorsal fin shape, and spots and other parameters of the Sousa Chinensis. Experimental results show that this study can effectively identify the dorsal fin of Sousa Chinensis and improve the work efficiency of Marine biologists." @default.
- W4366380935 created "2023-04-21" @default.
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- W4366380935 date "2022-12-16" @default.
- W4366380935 modified "2023-10-01" @default.
- W4366380935 title "Image Recognition Algorithm of Sousa Chinensis based on deep learning" @default.
- W4366380935 cites W1536680647 @default.
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- W4366380935 doi "https://doi.org/10.1145/3584376.3584574" @default.
- W4366380935 hasPublicationYear "2022" @default.
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