Sow uterine contraction behavior identification method based on machine vision

By using an improved SLP3D model and lightweight processing technology, the problems of insufficient generalization ability and high computational cost in sow uterine contraction behavior recognition have been solved, achieving efficient and low-cost sow status monitoring and meeting actual breeding needs.

CN120877386AActive Publication Date: 2025-10-31NAT CENT OF TECH INNOVATON FOR PIGNS +1
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Patent Information

Application Number
CN202511373532.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-25
Publication Date
2025-10-31
Estimated Expiration
2045-09-25

AI Technical Summary

Technical Problem

Existing technologies for recognizing sow uterine contractions suffer from insufficient generalization ability and high computational costs, making them difficult to apply effectively in real-world production environments and failing to meet the needs of agricultural farming.

Method used

The SLP3D model is combined with the lightweight spatial channel attention module LSCA and the partial bidirectional spatial pyramid feature fusion module PBFPN. The model is deployed on a backend server for real-time video processing to identify sow uterine contraction behavior. The Swish activation function is used to optimize the model, reducing computational complexity and resource requirements.

Benefits of technology

It significantly reduces computational overhead and equipment requirements while ensuring recognition accuracy, enabling real-time monitoring of sow status in actual production environments and improving breeding efficiency and economic benefits.

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Abstract

The invention provides a sow uterine contraction behavior recognition method based on machine vision, and relates to the field of image processing, and the method comprises the steps: S1, data collection, S2, data transmission, S3, model reasoning and processing, and S4, behavior detection and output. According to the method, the sow uterine contraction behavior can be automatically identified, supervision of the state of the to-be-delivered sow by first-line breeding personnel is facilitated, death of the to-be-delivered sow and newborn piglets is reduced, and economic benefits of breeding enterprises are improved; meanwhile, the method effectively balances the accuracy of sow uterine contraction behavior recognition and the complexity of the model, has high practicability, and meets the requirements of the actual production environment.
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