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SALC-Net: A lightweight contour-preserving segmentation network for yak body segmentation in complex grazing
Wang Zhang1, Changqi Fu2, Jiayi Xing1
1School of Mechanical Engineering, Qinghai University, Xining, China.
Plos One
|August 4, 2026
Summary
We developed SALC-Net, a lightweight AI model for precise yak segmentation, improving intelligent pasture management. This efficient network achieves high accuracy and real-time performance on edge devices.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Agricultural Technology
Background:
- Accurate livestock segmentation is vital for intelligent pasture management and non-contact image analysis.
- Lightweight networks are needed for edge devices but struggle with geometric adaptability and boundary preservation for non-rigid livestock.
Purpose of the Study:
- To propose SALC-Net, a lightweight segmentation framework for efficient and accurate yak body contour extraction.
- To address the limitations of existing lightweight networks in geometric adaptability and boundary preservation for livestock segmentation.
Main Methods:
- SALC-Net utilizes a re-parameterized MobileNetV2 backbone for efficient inference.
- Incorporates a Scale-Adaptive Efficient Dynamic Pyramid (SA-EDP) module for adaptive receptive-field modeling.
- Employs a Linear Cross-Scale Fusion (LCSF) module for contour-preserving feature reconstruction.
Main Results:
- SALC-Net achieved 93.37% mean Intersection over Union (mIoU) on a custom yak dataset.
- The framework demonstrated a processing speed of 129 frames per second (FPS).
- Results indicate a favorable balance between segmentation accuracy and real-time efficiency.
Conclusions:
- SALC-Net offers an effective solution for livestock segmentation on resource-constrained edge devices.
- The proposed framework enhances the reliability of downstream shape-related analyses for non-rigid livestock targets.
- SALC-Net provides a practical advancement for intelligent pasture management systems.