In-vivo fish counting by non-imaging system

Klapp, Iftach; Arad, Or; Rosenfeld, Lavi; Barki, Assaf; Shaked, Ben; Zion, Boaz

doi:10.1364/pv.2017.jw5a.11

Cited by 1 publication

(1 citation statement)

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“…Abinaya et al (2022) used YOLOv4 to segment fish to assess biomass. The species detection method based on deep learning information is accurate and efficient (Klapp et al, 2018;Mei et al, 2022) and can recognize the species and locate the object . However, this method still lacks morphological phenotypic feature recognition of specific object parts, hindering the complete phenotypic and shape segmentation.…”

Section: Species Detection Methods Based On Deep Learning Informationmentioning

confidence: 99%

RA-UNet: an intelligent fish phenotype segmentation method based on ResNet50 and atrous spatial pyramid pooling

Li,

Liu,

Yang

et al. 2023

Front. Environ. Sci.

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Introduction: Changes in fish phenotypes during aquaculture must be monitored to improve the quality of fishery resources. Therefore, a method for segmenting and measuring phenotypes rapidly and accurately without harming the fish is essential. This study proposes an intelligent fish phenotype segmentation method based on the residual network, ResNet50, and atrous spatial pyramid pooling (ASPP).Methods: A sufficient number of fish phenotypic segmentation datasets rich in experimental research was constructed, and diverse semantic segmentation datasets were developed. ResNet50 was then built as the backbone feature extraction network to prevent the loss of fish phenotypic feature information and improve the precision of fish phenotypic segmentation. Finally, an ASPP module was designed to improve the phenotypic segmentation accuracy of different parts of fish.Results: The test algorithm based on the collected fish phenotype segmentation datasets showed that the proposed algorithm (RA-UNet) yielded the best results among several advanced semantic segmentation models. The mean intersection over union (mIoU) and mean pixel accuracy (mPA) were 87.8% and 92.3%, respectively.Discussion: Compared with the benchmark UNet algorithm, RA-UNet demonstrated improvements in the mIoU and mPA by 5.0 and 1.8 percentage points, respectively. Additionally, RA-UNet exhibited superior fish phenotype segmentation performance, with a low false detection rate and clear and complete edge segmentation. Conclusively, the RA-UNet proposed in this study has high accuracy and edge segmentation ability and can, therefore, directly improve the efficiency of phenotypic monitoring in fish farming.

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Section: Species Detection Methods Based On Deep Learning Informationmentioning

confidence: 99%