Meat duck ingestion and drinking behavior recognition algorithm based on MOT and instance segmentation

By combining MOT and instance segmentation algorithms with TAM and Mask R-CNN networks, the problem of confusion in the recognition of meat duck behavior in a stacked cage environment is solved, achieving high-precision monitoring of feeding and drinking behavior, and supporting real-time analysis of health status and early warning of anomalies.

CN120877330APending Publication Date: 2025-10-31JIANGSU ACAD OF AGRI SCI
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Patent Information

Application Number
CN202511017357.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-23
Publication Date
2025-10-31

AI Technical Summary

Technical Problem

In a stacked cage environment, traditional single-target tracking and simple segmentation methods are difficult to accurately distinguish between the feeding and drinking behaviors of meat ducks, leading to confusion in behavior recognition and affecting health monitoring and disease prevention and control.

Method used

We employ recognition algorithms based on MOT and instance segmentation, combining a TAM network structure for multi-target tracking and a Mask R-CNN network structure for behavior recognition and detection. We optimize mask generation using SAM and XMem models to achieve pixel-level target segmentation and behavior recognition.

Benefits of technology

It improves the accuracy and speed of identifying the feeding and drinking behavior of meat ducks, reduces costs, enhances monitoring efficiency, and supports real-time health status monitoring and early warning of abnormal behavior.

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Abstract

The invention provides a meat duck ingestion and drinking behavior recognition algorithm based on MOT and instance segmentation, and the method comprises the steps: collecting and marking an image of the ingestion and drinking behavior of a meat duck, and building a caged meat duck ingestion and drinking behavior detection data set through image data preprocessing; meat duck group multi-target segmentation and tracking based on target perception TAM; constructing and training a meat duck ingestion and drinking behavior recognition and detection network based on a Mask R-CNN algorithm; and classifying and judging the behaviors of the meat ducks, and counting the total frame number of the water drinking behaviors and the total frame number of the feeding behaviors of the target meat ducks in the video detection period to obtain the water drinking duration and the feeding duration. According to the invention, high-precision identification and time duration statistics of meat duck ingestion and water drinking behaviors can be realized in a stacked cage culture environment, the meat duck health state perception and fine management level of a farm can be improved, abnormal behaviors can be early warned in time, scientific feeding and risk control can be assisted, and the method has good practicability and popularization value.
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