ArticlePloS one2026
SALC-Net: A lightweight contour-preserving segmentation network for yak body segmentation in complex grazing environments.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate livestock segmentation is a key prerequisite for non-contact image-based analysis and intelligent pasture management, yet remains challenging on resource-constrained edge devices. Although lightweight networks are suitable for real-time deployment, they often suffer from limited geometric adaptability and insufficient boundary preservation, which reduces the reliability of downstream shape-related analysis for non-rigid livestock targets. To address this issue, we propose SALC-Net, a lightweight segmentation framework for yak body contour extraction. SALC-Net combines a re-parameterized MobileNetV2 backbone for efficient inference, a Scale-Adaptive Efficient Dynamic Pyramid (SA-EDP) module for low-cost adaptive receptive-field modeling, and a Linear Cross-Scale Fusion (LCSF) module for contour-preserving feature reconstruction. Experiments on a custom high-altitude yak dataset show that SALC-Net achieves 93.37% mIoU at 129 FPS, demonstrating a favorable trade-off between segmentation accuracy and real-time efficiency.
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