<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The behaviours of the pig are often closely related to their health. Pig recognition is very important for pig behaviour analysis and digital breeding. Currently, the early signs and abnormal behaviours of sick pigs in breeding farms are mainly completed by human observation. However, visual inspection is labour intensive and time-consuming, and it suffers from the problems of individual experiences and varying environments. An improved ResNet model was proposed and applied to detect individual pigs in this study based on deep learning knowledge. The developed model captured the features of pigs applying across layer connections, and the ability of feature expression was improved by adding a new residual module. The number of layers was reduced to minimise the net complexity. Generally, the ResNet frame was developed by reducing the number of convolution layers, constructing different types of the residual module and adding the number of convolution kernels. The training accuracy and testing accuracy reached 98.2% and 96.4%, respectively, when using the improved model. The experiment results showed that the method proposed in this paper for checking living situations and disease prevention of commercial pigs in pig farms is potential.</p>
</abstract>

The behaviours of the pig are often closely related to their health. Pig recognition is very important for pig behaviour analysis and digital breeding. Currently, the early signs and abnormal behaviours of sick pigs in breeding farms are mainly completed by human observation. However, visual inspection is labour intensive and time-consuming, and it suffers from the problems of individual experiences and varying environments. An improved ResNet model was proposed and applied to detect individual pigs in this study based on deep learning knowledge. The developed model captured the features of pigs applying across layer connections, and the ability of feature expression was improved by adding a new residual module. The number of layers was reduced to minimise the net complexity. Generally, the ResNet frame was developed by reducing the number of convolution layers, constructing different types of the residual module and adding the number of convolution kernels. The training accuracy and testing accuracy reached 98.2% and 96.4%, respectively, when using the improved model. The experiment results showed that the method proposed in this paper for checking living situations and disease prevention of commercial pigs in pig farms is potential.

Detection of pig based on improved RESNET model in natural scene

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Detection of pig based on improved RESNET model in natural scene"}

The behaviours of the pig are often closely related to their health. Pig recognition is very important for pig behaviour analysis and digital breeding....

Detection of pig based on improved RESNET model in natural scene

Pubblicato online: 30 ott 2021

Pagine: 215 - 226

Ricevuto: 26 mar 2021

Accettato: 29 giu 2021

DOI: https://doi.org/10.2478/amns.2021.2.00040

Parole chiave
deep learning, object detection, ResNet model, feature classification

© 2021 Weixian Song et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Detection of pig based on improved RESNET model in natural scene

Pubblicato online: 30 ott 2021

Pagine: 215 - 226

Ricevuto: 26 mar 2021

Accettato: 29 giu 2021

DOI: https://doi.org/10.2478/amns.2021.2.00040

Parole chiavedeep learning, object detection, ResNet model, feature classification

© 2021 Weixian Song et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
deep learning, object detection, ResNet model, feature classification