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Topic

Technologies and technical equipment for agriculture and food industry

Volume

Volume 69 / No. 1 / 2023

Pages : 88-98

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ANOMALY DETECTION FOR HERD PIGS BASED ON YOLOX

基于YOLOX的群养猪只异常检测

DOI : https://doi.org/10.35633/inmateh-69-08

Authors

Yanwen LI

College of Information Science and Engineering, Shanxi Agricultural University, Taigu/China

(*) Juxia LI

College of Information Science and Engineering, Shanxi Agricultural University, Taigu/China

Zhenyu LIU

Graduate School of Shanxi Agricultural University, Shanxi / China

Zhifang BI

Basic Department, Shanxi Agricultural University, Shanxi / China

Hui ZHANG

College of Information Science and Engineering, Shanxi Agricultural University, Taigu/China

Lei DUAN

College of Information Science and Engineering, Shanxi Agricultural University, Taigu/China

(*) Corresponding authors:

Abstract

In order to solve the problem that the complex pig house environment leads to the difficulty and low accuracy of abnormal detection of group pigs. The video of 9 adult fattening pigs were collected, and the video key frames were obtained by the frame differential method as the training set, and the YOLOX model for abnormal detection of group pigs was constructed. The results show that the average accuracy of YOLOX model on the test set is 98.0%. The research results can provide a reference for the detection of pig anomalies in the breeding environment of pig farms.

Abstract in Chinese

为了解决复杂的猪舍环境导致对群养猪只异常检测困难和准确率较低的问题。采集9头成年育肥猪视频图像,采用帧间差分法获取视频关键帧作为训练集,构建群养猪只异常检测YOLOX模型。结果表明,YOLOX网络模型在测试集上平均准确率达98.0%。研究结果可为猪场养殖环境中针对猪只异常检测提供参考。

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