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Related Experiment Video

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In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals

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Sheep Face Detection Based on an Improved RetinaFace Algorithm.

Jinye Hao¹, Hongming Zhang¹, Yamin Han¹

¹College of Information Engineering, Northwest A&F University, Xianyang 712100, China.

Animals : an Open Access Journal From MDPI

|August 12, 2023

Summary

This summary is machine-generated.

This study introduces an improved RetinaFace algorithm for accurate sheep face detection, enhancing livestock management and food traceability. The method achieves high performance in real-world conditions, overcoming challenges like varied lighting and angles.

Keywords:

attention module computer vision improved RetinaFace lightweight sheep face detection

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Area of Science:

Computer Vision
Artificial Intelligence
Livestock Management

Background:

Accurate sheep identification is crucial for food traceability and preventing fraud.
Sheep face detection shows promise but faces challenges due to illumination, angles, and scale variations.
Existing methods struggle with real-world farm conditions.

Purpose of the Study:

To develop an effective and lightweight sheep face detection method for real-time application on farms.
To improve the accuracy and speed of sheep face detection.
To address the limitations of current algorithms in diverse environmental conditions.

Main Methods:

An improved RetinaFace algorithm was proposed, utilizing an enhanced MobileNetV3-large backbone with switchable atrous convolution for faster multi-scale feature extraction.
Channel and spatial attention modules were integrated into the detector to emphasize key sheep facial features.
The algorithm was tested on a dataset collected from real-world sheep farm scenarios.

Main Results:

The proposed method achieved an F1-score of 95.25% and an average precision of 96.00%.
The model is lightweight with a size of 13.20 M and 3.20 M parameters.
It demonstrated an average processing time of 26.83 ms, enabling real-time detection.

Conclusions:

The improved RetinaFace algorithm effectively addresses challenges in sheep face detection, offering high accuracy and speed.
This method provides a robust solution for sheep identification in practical agricultural settings.
The lightweight design makes it suitable for deployment on actual sheep farms.