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Lightweight network based on Fourth order Runge-Kutta scheme and Hybrid Attention Module for pig face recognition.

Authors :
Guo, Jianjun
Kong, Yiyou
Lin, Lijun
Xu, Longqin
Feng, Dachun
Cao, Liang
Chen, Jiexin
Ye, Junwei
Ye, Shuqing
Yao, Zhaozhong
Liu, Yue
Liu, Tonglai
Liu, Shuangyin
Source :
Computers & Electronics in Agriculture. Aug2024, Vol. 223, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• A lightweight model for pig face recognition was developed using a fourth-order Runge-Kutta numerical method and a hybrid attention module. Subsequent experiments were conducted to evaluate its performance. • The model effectively enhances training and inference efficiency, reduces the number of parameters and memory usage, and improves accuracy in the extraction of pig face information. • Experimental tests show that the model achieves 99.26 % recognition accuracy and has a size of only 1.52 MB, which makes it suitable for deployment on embedded devices. • The heatmap visualization of the model's attention to the region of interest shows that the model pays more attention to the pig face region compared to other models. • The model can provide technical support for the scientific management of pig farms, thus improving the feasibility of deploying lightweight intelligent devices in smart pig farms. Pig face recognition plays a significant role in intelligent pig farming. Accurate and lightweight methods for recognizing pig faces are crucial for precision pig farming. Generally, under the condition of the same computing resources, the lower the image resolution, the faster the model inference speed. Based on the above idea, this paper proposes a lightweight pig face model called RKNet-HAM, based on the fourth-order Runge-Kutta and hybrid attention mechanism. This model exhibits the most prominent focus on the semantic information of low-resolution(64 × 64) pig face images and achieves high accuracy in recognizing similar pig faces with a low error classification rate. A dataset of 21,742 images of 32 pigs was constructed for individual pig recognition. The proposed RKNet-HAM model and several other deep learning models, e.g., RKNet-CBAM, RKNet, ShuffleNetV2.0, DesNet121, and ResNet50. RKNet-HAM has a size of 1.52 Megabytes. Experimental results show that among these models, the proposed model ofachieved the highest accuracy, precision, recall, and specificity, with an accuracy rate of 99.26 %. RKNet-HAM also exhibits good generalization ability. It provides experimental support for mobile and embedded applications. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01681699
Volume :
223
Database :
Academic Search Index
Journal :
Computers & Electronics in Agriculture
Publication Type :
Academic Journal
Accession number :
177856746
Full Text :
https://doi.org/10.1016/j.compag.2024.109099