#5921. Feature detection method for hind leg segmentation of sheep carcass based on multi-scale dual attention U-Net

August 2026publication date
Proposal available till 03-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Horticulture;
Agronomy and Crop Science;
Forestry;
Computer Science Applications;
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Abstract:
Due to the variable size of the sheep carcass and the complex characteristics of the surface tissue of the hind legs, the recognition accuracy of the segmented target muscle area is low. This paper proposes a method for detecting the segmentation features of sheep carcass hind legs and carries out a segmentation test to validate it. The approach takes the multi-scale dual attention U-Net (MDAU-Net) semantic segmentation network as its core. It effectively combines different layer features, spatial attention modules, and channel attention modules. We design a multi-scale dual attention (MDA) module to enhance multi-scale contextual semantic and local detail features, and embeds it into the U-Net hopping layer connection to obtain the specific semantic features and local details features of the coding stage.
Keywords:
Deep learning; Intelligent segmentation; MDAU-Net; Semantic segmentation; Sheep carcass hind legs

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