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Live pig ingestion behavior analysis method

A behavior analysis and feeding technology, applied in the field of deep learning target detection and behavior recognition, can solve the problem of weak feature extraction ability, and achieve the effect of good detection effect and improved detection effect.

Pending Publication Date: 2020-08-07
HANGZHOU DIANZI UNIV
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  • Claims
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Problems solved by technology

But because of its simple network, although the running speed is fast, the feature extraction ability is weak

Method used

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Embodiment Construction

[0024] The present invention will be further limited below in conjunction with the accompanying drawings and embodiments, but not limited thereto.

[0025] The training of model among the present invention is carried out on a server, Ubuntu16.04 environment, graphics card is NVIDIAGeForce 1080Ti 11GB, CUDA9.0, cuDNN7.0, based on TensorFlow_gpu1.8.0 framework, image preprocessing adopts OpenCV library.

[0026] Such as figure 1 The flow shown is a research method based on the improved YOLOv3-Tiny analysis of pig feeding behavior, including the following steps:

[0027] (1) Obtain the video of group-raised pigs. For individual pigs eating in the video frame images, manually mark their location frame and category information, and preprocess the target image data to construct a data set. Specifically, SLR cameras are used to shoot videos in the pig house to obtain videos including individual pigs’ feeding and non-feeding behaviors, and two long and short videos are collected for ...

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Abstract

The invention discloses a live pig ingestion behavior analysis method. The method comprises the following steps: acquiring group cultured live pig videos, and constructing a data set; carrying out preprocessing and data enhancement on the target data set, manually markingthe position frame and category information of the ingested pigs, and carrying outK-means clustering anchorfor the pig form; andtraining by using an improved YOLOv3-tiny algorithm, wherein the improved YOLOv3-ty model is a model obtained after dense connection improvement is carried out in combination with a ResNet thought and a small target detection scale is added; acquiring a to-be-tested live pig ingestion image and inputting the ingestion image into the trained model for detection to obtain a result image. Compared with a method before improvement, the detection precision is improved on the basis of ensuring certain detection real-time performance.

Description

technical field [0001] The invention belongs to the field of deep learning target detection and behavior recognition, and in particular relates to a research method based on the improved YOLOv3-Tiny analysis of pig feeding behavior. [0002] technical background [0003] The rapid development of deep learning has made new breakthroughs in a series of algorithms such as target detection, positioning, and classification, providing powerful technical means for the detection, identification, and behavior analysis of breeding objects, and the non-contact research means are more scientific and humane. AI can help farmers identify various behaviors of pigs, such as judging the eating, sleeping, sickness or death of the target in each frame of the video, and can establish growth and health files for each pig through the detection-identification-tracking process, and Improve pig welfare by changing feeding standards according to their growth status. There have been a large number of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/10G06N3/045G06F18/23213G06F18/241
Inventor 秦兴王子枫
Owner HANGZHOU DIANZI UNIV
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