Artificial Intelligence in New Zealand: applications and innovation

Xue, Bing; Green, Richard; Zhang, Mengjie

doi:10.1080/03036758.2023.2170165

“…For instance, researchers can find desired features like disease resistance, rapid growth, and environmental adaptation by sequencing the genomes of several fish populations and examining genetic variants. Researchers can choose the best candidates for breeding with the use of AI, which can make the process much quicker and more precise than conventional techniques ( Xue et al, 2023 ). This will ultimately increase the productivity and sustainability of aquaculture operations.…”

Section: Potential Applications Of Artificial Intelligence In Aquacul...mentioning

confidence: 99%

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

Ashraf Rather,

Ahmad,

Shah

et al. 2024

Food Chemistry: X

3

0

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“…Computer vision has been widely used in aquaculture, such as aquaculture seedling counting [1], aquatic product measurement, and fish behavior detection [2]. Semantic segmentation is one of the crucial directions in deep learning research.…”

Section: Introductionmentioning

confidence: 99%

UISS-Net: Underwater Image Semantic Segmentation Network for improving boundary segmentation accuracy of underwater images

He,

Cao,

Luo

et al. 2024

Aquacult Int

1

0

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Image semantic segmentation t is widely used in aquatic product measurement, aquatic biological cell segmentation, and aquatic biological classifications. However, underwater image segmentation has low accuracy and poor robustness because of turbid underwater environments and insufficient light. Therefore, this paper proposes an Underwater Image Semantic Segmentation Network (UISS-Net) for underwater scenes. Firstly, the backbone network uses an auxiliary feature extraction network to improve the extraction of semantic features for the backbone network. Secondly, the channel attention mechanism enhances the vital attention information during feature fusion. Then, multi-stage feature input up-sampling is used to recover better semantic features in the network during up-sampling. Finally, the cross-entropy loss function and dice loss function are used to focus on the boundary semantic information of the target. The experimental results show that the network effectively improves the boundary of the target object after segmentation, avoids aliasing with other classes of pixels, improves the segmentation accuracy of the target boundary, and retains more feature information. The mIoU and mPA of UISS-Net in the semantic Segmentation of Underwater IMagery (SUIM) dataset achieve 72.09% and 80.37%, respectively,

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UISS-Net:Underwater Image Semantic Segmentation Network for Improving Boundary Segmentation Accuracy of Underwater Images

he,

Cao,

Luo

et al. 2023

Preprint

0

View full text Add to dashboard Cite

Image semantic segmentation t is widely used in aquatic product measurement, aquatic biological cell segmentation, and aquatic biological classifications. However, underwater image segmentation has low accuracy and poor robustness because of turbid underwater environments and insufficient light. Therefore, this paper proposes an Underwater Image Semantic Segmentation Network (UISS-Net) for underwater scenes. Firstly, the backbone network uses an auxiliary feature extraction network to improve the extraction of semantic features for the backbone network. Secondly, the channel attention mechanism enhances the vital attention information during feature fusion. Then, multi-stage feature input up-sampling is used to recover better semantic features in the network during up-sampling. Finally, the cross-entropy loss function and dice loss function are used to focus on the boundary semantic information of the target. The experimental results show that the network effectively improves the boundary of the target object after segmentation, avoids aliasing with other classes of pixels, improves the segmentation accuracy of the target boundary, and retains more feature information. The mIoU and mPA of UISS-Net in the semantic Segmentation of Underwater IMagery (SUIM) dataset achieve 72.09% and 80.37%, respectively, 9.68% and 7.63% higher than the baseline model. In the Deep Fish dataset, UISS-Net achieved 95.05% mIoU, 12.3% higher than the baseline model.

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Artificial Intelligence in New Zealand: applications and innovation

Cited by 3 publications

References 15 publications

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

UISS-Net: Underwater Image Semantic Segmentation Network for improving boundary segmentation accuracy of underwater images

UISS-Net:Underwater Image Semantic Segmentation Network for Improving Boundary Segmentation Accuracy of Underwater Images

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