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PloS one2025; 20(3); e0318498; doi: 10.1371/journal.pone.0318498

Detection of mare parturition through balanced multi-scale feature fusion based on improved Libra RCNN.

Abstract: Once a mare experiences parturition abnormalities, the outcome between a live foal and a stillborn can change rapidly. Automated detection of mare parturition and timely human intervention is crucial to reducing risks during mare and foal parturition. This paper addresses the challenges of manual monitoring of parturition in large-scale equine facilities due to the unpredictability of mare parturition timing, proposing an algorithm for detecting mare parturition through a balanced multi-scale feature fusion based on an improved Libra RCNN. Initially, a ResNet101 backbone network incorporating the CBAM attention module was used to enhance parturition feature extraction capability; subsequently, a balanced content-aware feature reassembly feature pyramid, CARAFE-BFP, was employed to mitigate data imbalance effects while enhancing the quality of feature map upsampling; finally, the GRoIE module was utilized to merge CARAFE-BFP's multi-scale features, improving the model's perception of multi-scale objectives and minor feature changes. The model achieved a mean average precision of 86.26% in scenarios of imbalanced positive and negative samples of mare parturition data, subtle parturition feature differences, and multi-scale data distribution, with a detection speed of 15.06 images per second and an average recall rate of 98.17%. Moreover, this study employed a statistical method combined with a sliding window mechanism to assess the algorithm's performance in detecting mare parturition in video stream continuous monitoring scenarios, achieving an accuracy rate of 92.75% for mare parturition detection. The algorithm proposed in this study achieved non-contact, stress-free, intensive, and automated detection of mare parturition, also demonstrating the immense potential of artificial intelligence technology in the field of animal production management.
Copyright: © 2025 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Publication Date: 2025-03-04 PubMed ID: 40036288PubMed Central: PMC11878943DOI: 10.1371/journal.pone.0318498Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This research presents a new artificial intelligence algorithm designed to automatically detect when a mare (female horse) is about to give birth, improving monitoring and reducing risks associated with equine parturition.
  • The proposed method enhances feature extraction and balances multi-scale data using an improved Libra RCNN framework, achieving high accuracy and real-time detection speeds.

Background and Motivation

  • Parturition abnormalities in mares can critically influence whether a foal is born alive or stillborn.
  • Timely detection of mare labor onset is essential for prompt human intervention to reduce health risks for both mare and foal.
  • Current monitoring methods often rely on manual observation in large-scale equine facilities, which is labor-intensive and inefficient due to unpredictability of the timing.
  • Automated, non-contact, and real-time detection systems based on video data offer a solution to these challenges.

Research Problem and Challenges

  • Detecting mare parturition from video involves several challenges:
    • Imbalanced datasets: far fewer positive parturition samples versus negatives.
    • Subtle visual differences indicating parturition onset.
    • Multi-scale data distribution: features appear at different spatial scales in the images.
  • These challenges complicate accurate detection of parturition events using existing computer vision models.

Methodology and Algorithm Design

  • The authors proposed an improved Libra RCNN-based detection model incorporating several advanced components to address these challenges:
  • Backbone Network – ResNet101 with CBAM Attention Module:
    • ResNet101 serves as the feature extractor; it is a deep convolutional neural network known for strong representational power.
    • The Convolutional Block Attention Module (CBAM) helps the network focus on important spatial and channel-wise features related to parturition cues.
  • Balanced Content-Aware Feature Reassembly Feature Pyramid (CARAFE-BFP):
    • This component handles multi-scale features, improving upsampling quality in feature maps.
    • It also balances the feature content to mitigate dataset imbalance problems, helping to detect both common and rare features effectively.
  • GRoIE Module (Generalized Region of Interest Enhancement):
    • Designed to merge multi-scale features from CARAFE-BFP.
    • Enhances the model’s sensitivity to multi-scale objects and subtle changes related to parturition.

Performance and Results

  • The algorithm achieved a mean average precision (mAP) of 86.26% despite difficult conditions of imbalanced datasets and subtle feature differences.
  • Detection speed reached 15.06 images per second, suitable for near real-time monitoring.
  • Recall rate was 98.17%, indicating very effective detection of true parturition events.
  • Additionally, a statistical method combined with a sliding window mechanism was proposed to analyze continuous video streams, improving detection reliability over time.
  • In video monitoring scenarios, the method achieved a 92.75% accuracy rate for mare parturition detection.

Significance and Implications

  • The study introduces a non-contact and stress-free automated system for intensive monitoring of mare parturition, reducing reliance on laborious manual observation.
  • Demonstrates the potential of AI-based object detection technologies in improving animal production management and welfare.
  • The balanced multi-scale feature fusion approach addresses common challenges in medical and veterinary image-based detection tasks, suggesting applicability to other domains with imbalanced and subtle visual data.

Summary

  • The research advances automated detection of critical animal physiological events utilizing deep learning and enhanced multi-scale feature fusion techniques.
  • It offers a practical solution for monitoring mare labor with high accuracy and speed, supporting timely intervention and thus improved outcomes for mares and foals.

Cite This Article

APA
Wang B, Duan W, Zhao J, Bai D. (2025). Detection of mare parturition through balanced multi-scale feature fusion based on improved Libra RCNN. PLoS One, 20(3), e0318498. https://doi.org/10.1371/journal.pone.0318498

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 20
Issue: 3
Pages: e0318498
PII: e0318498

Researcher Affiliations

Wang, Buyu
  • College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.
  • Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China.
Duan, Weijun
  • College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.
Zhao, Jian
  • College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.
Bai, Dongyi
  • College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.

MeSH Terms

  • Animals
  • Horses / physiology
  • Female
  • Parturition / physiology
  • Pregnancy
  • Algorithms
  • Neural Networks, Computer

Conflict of Interest Statement

The authors have declared that no competing interests exist.

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