Research on online detection method of total mixed ration classification

Shan Ni; Kuanmin Mao; Dongfeng Zhang

doi:10.1117/12.3041682

10 September 2024 Research on online detection method of total mixed ration classification

Shan Ni, Kuanmin Mao, Dongfeng Zhang

Author Affiliations +

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325719 (2024) https://doi.org/10.1117/12.3041682
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

At present, the traditional method of eligibility classification of TMR is mainly operated manually, which is time-consuming and labor-intensive. This paper adopts the method of image classification based on deep learning for online detection of TMR classification. This article first constructs the TMR data set, and then uses GoogLeNet, ResNet and SEnet three classic network models to conduct preliminary experiments. According to the experimental results, the optimal model is selected to improve the model in terms of accuracy and speed. The improved model has an accuracy rate of 96.82%, and the speed basically meets the practical needs, providing a theoretical basis for the subsequent research and development of intelligent TMR batching systems and equipment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shan Ni, Kuanmin Mao, and Dongfeng Zhang "Research on online detection method of total mixed ration classification", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325719 (10 September 2024); https://doi.org/10.1117/12.3041682

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KEYWORDS

RGB color model

Convolution

Education and training

Image fusion

Deep learning

Image enhancement

Data modeling

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