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Posture Detection of Individual Pigs Based on Lightweight Convolution Neural Networks and Efficient Channel-Wise Attention

文献类型： 外文期刊

作者： Luo, Yizhi 1 ; Zeng, Zhixiong 1 ; Lu, Huazhong 2 ; Lv, Enli 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China

2.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China

关键词： pig postures; real-time detection; lightweight model; channel-wise attention

期刊名称：SENSORS （ 影响因子：3.847； 五年影响因子：4.05 ）

ISSN：

年卷期： 2021 年 21 卷 24 期

页码：

收录情况： SCI

摘要： In this paper, a lightweight channel-wise attention model is proposed for the real-time detection of five representative pig postures: standing, lying on the belly, lying on the side, sitting, and mounting. An optimized compressed block with symmetrical structure is proposed based on model structure and parameter statistics, and the efficient channel attention modules are considered as a channel-wise mechanism to improve the model architecture.The results show that the algorithm's average precision in detecting standing, lying on the belly, lying on the side, sitting, and mounting is 97.7%, 95.2%, 95.7%, 87.5%, and 84.1%, respectively, and the speed of inference is around 63 ms (CPU = i7, RAM = 8G) per postures image. Compared with state-of-the-art models (ResNet50, Darknet53, CSPDarknet53, MobileNetV3-Large, and MobileNetV3-Small), the proposed model has fewer model parameters and lower computation complexity. The statistical results of the postures (with continuous 24 h monitoring) show that some pigs will eat in the early morning, and the peak of the pig's feeding appears after the input of new feed, which reflects the health of the pig herd for farmers.

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