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Related Experiment Video

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Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

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Cross-Breed Few-Shot Learning for Pig Detection via Improved YOLOv7 and CycleGAN-Based Sample Generation.

Yizheng Zhuang^1,2, Lingyao Xu¹, Jinyun Jiang¹

¹College of Animal Sciences, Zhejiang University, Hangzhou 310058, China.

|April 27, 2026

Summary

This study introduces a novel few-shot pig detection framework using an enhanced YOLOv7 detector and CycleGAN for synthetic image generation. The approach significantly improves cross-breed pig detection accuracy in challenging farming environments.

Keywords:

CycleGAN augmentation YOLOv7 optimization few-shot learning pig detection precision farming

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Published on: May 21, 2020

Area of Science:

Computer Vision
Artificial Intelligence
Agricultural Technology

Background:

Robust pig detection is hindered by complex farm environments, breed variations, and high annotation costs.
Few-shot, cross-breed pig detection remains an underexplored area in automated livestock monitoring.

Purpose of the Study:

To develop an effective few-shot pig detection framework for cross-breed scenarios.
To address limitations in manual annotation and breed diversity for pig detection systems.

Main Methods:

Proposed a framework combining an improved YOLOv7 detector with CycleGAN-based pseudo-sample generation.
Enhanced YOLOv7 with anchor optimization, Efficient Channel Attention (ECA), and Log-Sum-Exp (LSE) pooling.
Utilized an optimized CycleGAN with perceptual loss for generating synthetic Duroc-like pig images.

Main Results:

Achieved 98.16% mean Average Precision (mAP) on a White pig dataset.
Reached 85.52% mAP on a Duroc Pig Few-Shot Dataset under a 10-shot setting.
Outperformed Faster R-CNN, CenterNet, YOLOv8, DCGAN, and SRGAN on the Duroc Few-Shot Dataset.

Conclusions:

The proposed method offers a practical solution for cross-breed few-shot pig detection.
Demonstrates significant potential for intelligent livestock monitoring in data-scarce conditions.
Highlights the effectiveness of combining advanced object detection with generative adversarial networks for agricultural applications.