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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Sustainable Self-Training Pig Detection System with Augmented Single Labeled Target Data for Solving Domain Shift

Junhee Lee¹, Heechan Chae¹, Seungwook Son¹

¹Info Valley Korea Co., Ltd., Anyang 14067, Republic of Korea.

Sensors (Basel, Switzerland)

|September 19, 2025

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This summary is machine-generated.

This study introduces a novel domain adaptation method for automated pig monitoring systems. The approach significantly enhances pig detection accuracy in diverse farm environments using minimal labeled data.

Area of Science:

Computer Vision
Machine Learning
Agricultural Technology

Background:

Rising global pork consumption necessitates efficient livestock management.
Deep learning models are increasingly used for automated pigsty monitoring.
Domain shift due to environmental variations (lighting, camera angles, density) severely degrades model performance.

Purpose of the Study:

To address the performance degradation of deep learning models in real-world pigsty environments.
To develop a cost-effective domain adaptation method requiring only a single labeled target (SLOT) sample.
To enable robust and generalized pig detection across diverse farming conditions without extensive data labeling.

Main Methods:

A self-training-based domain adaptation method utilizing a single labeled target (SLOT) sample.

Keywords:

data augmentation domain shift genetic algorithm monitoring system object detection self-training

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Last Updated: Jan 17, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Genetic algorithm (GA)-based data augmentation search (DAS) optimized for SLOT data.

A super-low-threshold strategy to incorporate low-confidence pseudo-labels during self-training.

Main Results:

Significant improvement in average precision (AP) from 36.86 to 90.62 under domain shift conditions.
Achieved performance comparable to fully supervised learning using only SLOT data.
Demonstrated robust detection performance across various pig-farming environments and stable performance under domain shifts.

Conclusions:

The proposed SLOT-based domain adaptation method effectively overcomes domain shift challenges in pigsty monitoring.
The system offers a feasible and efficient solution for real-world agricultural applications.
This approach significantly reduces the need for large-scale, costly data labeling in livestock management systems.