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- W4366549503 abstract "Automated monitoring and analysis of fish’s growth status and behaviors can help scientific aquaculture management and reduce severe losses due to diseases or overfeeding. With developments in machine vision and deep learning (DL) techniques, DL-based object detection techniques have been extensively applied in aquaculture with the advantage of simultaneously classifying and localizing fish of interest in images. This study reviews the relevant research status of DL-based object detection techniques in fish counting, body length measurement, and individual behavior analysis in aquaculture. The research status is summarized from two aspects: image and video analysis. Moreover, the relevant technical details of DL-based object detection techniques applied to aquaculture are also summarized, including the dataset, image preprocessing methods, typical DL-based object detection algorithms, and evaluation metrics. Finally, the challenges and potential trends of DL-based object detection techniques in aquaculture are concluded and discussed. The review shows that generic DL-based object detection architectures have played important roles in aquaculture." @default.
- W4366549503 created "2023-04-22" @default.
- W4366549503 creator A5031145172 @default.
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- W4366549503 creator A5041790532 @default.
- W4366549503 creator A5048028685 @default.
- W4366549503 creator A5060769407 @default.
- W4366549503 date "2023-04-20" @default.
- W4366549503 modified "2023-09-25" @default.
- W4366549503 title "Application of Deep Learning-Based Object Detection Techniques in Fish Aquaculture: A Review" @default.
- W4366549503 cites W1536680647 @default.
- W4366549503 cites W1965718792 @default.
- W4366549503 cites W2040255112 @default.
- W4366549503 cites W2070723007 @default.
- W4366549503 cites W2102605133 @default.
- W4366549503 cites W2108598243 @default.
- W4366549503 cites W2131251535 @default.
- W4366549503 cites W2150406592 @default.
- W4366549503 cites W2290108831 @default.
- W4366549503 cites W2315444184 @default.
- W4366549503 cites W2423811253 @default.
- W4366549503 cites W2522256182 @default.
- W4366549503 cites W2570343428 @default.
- W4366549503 cites W2589255103 @default.
- W4366549503 cites W2615126350 @default.
- W4366549503 cites W2747582412 @default.
- W4366549503 cites W2766648222 @default.
- W4366549503 cites W2767603489 @default.
- W4366549503 cites W2786808285 @default.
- W4366549503 cites W2797538836 @default.
- W4366549503 cites W2883905828 @default.
- W4366549503 cites W2884367402 @default.
- W4366549503 cites W2895671046 @default.
- W4366549503 cites W2919115771 @default.
- W4366549503 cites W2920741983 @default.
- W4366549503 cites W2921271497 @default.
- W4366549503 cites W2922835573 @default.
- W4366549503 cites W2941123315 @default.
- W4366549503 cites W2944299331 @default.
- W4366549503 cites W2963037989 @default.
- W4366549503 cites W2963150697 @default.
- W4366549503 cites W2963377935 @default.
- W4366549503 cites W2967802864 @default.
- W4366549503 cites W2977808367 @default.
- W4366549503 cites W2981482662 @default.
- W4366549503 cites W2982993655 @default.
- W4366549503 cites W2991363140 @default.
- W4366549503 cites W2995304823 @default.
- W4366549503 cites W2997127923 @default.
- W4366549503 cites W3008350376 @default.
- W4366549503 cites W3013664842 @default.
- W4366549503 cites W3017145925 @default.
- W4366549503 cites W3019461574 @default.
- W4366549503 cites W3033387047 @default.
- W4366549503 cites W3042655494 @default.
- W4366549503 cites W3047829443 @default.
- W4366549503 cites W3082738557 @default.
- W4366549503 cites W3082843304 @default.
- W4366549503 cites W3083557050 @default.
- W4366549503 cites W3084275540 @default.
- W4366549503 cites W3091847338 @default.
- W4366549503 cites W3094321741 @default.
- W4366549503 cites W3106228955 @default.
- W4366549503 cites W3106250896 @default.
- W4366549503 cites W3113410735 @default.
- W4366549503 cites W3122592650 @default.
- W4366549503 cites W3128967214 @default.
- W4366549503 cites W3140854437 @default.
- W4366549503 cites W3149592957 @default.
- W4366549503 cites W3151882058 @default.
- W4366549503 cites W3152753831 @default.
- W4366549503 cites W3201469925 @default.
- W4366549503 cites W3209526300 @default.
- W4366549503 cites W3213506477 @default.
- W4366549503 cites W3215753146 @default.
- W4366549503 cites W3215918889 @default.
- W4366549503 cites W4220811997 @default.
- W4366549503 cites W4226514331 @default.
- W4366549503 cites W4283023529 @default.
- W4366549503 cites W4283399003 @default.
- W4366549503 cites W4285384934 @default.
- W4366549503 cites W4288375226 @default.
- W4366549503 cites W4289516210 @default.
- W4366549503 cites W4293860021 @default.
- W4366549503 cites W4293877372 @default.
- W4366549503 cites W4297820416 @default.
- W4366549503 cites W4298130902 @default.
- W4366549503 cites W4307685392 @default.
- W4366549503 cites W4307852657 @default.
- W4366549503 cites W4309400993 @default.
- W4366549503 cites W4309497642 @default.
- W4366549503 cites W4311105417 @default.
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- W4366549503 doi "https://doi.org/10.3390/jmse11040867" @default.
- W4366549503 hasPublicationYear "2023" @default.
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