Modified omni-scale net architecture for cattle identification on their muzzle point image pattern characteristics

Starasotnikau Uladzislau; Xin Feng

doi:10.1117/12.2681201

23 May 2023 Modified omni-scale net architecture for cattle identification on their muzzle point image pattern characteristics

Starasotnikau Uladzislau, Xin Feng

Author Affiliations +

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264520 (2023) https://doi.org/10.1117/12.2681201
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Animal biometrics is a frontier field of computer vision, pattern recognition and cognitive science that plays a vital role in the registration, unique identification and verification of livestock (cattle). In this study, we propose a deep learning approach to cattle identification based on the characteristics of the muzzle point image (nose pattern) to solve the problem of missed or replaced animals and false insurance claims. Inspired on the state-of-the-art OSNet architecture, which was developed for person re-identification, we introduce significant modifications in order to improve accuracy in the cattle recognition problem. First, each convolution layer is replaced by a depth-wise separable convolution layer, and parametric rectified linear unit is used as a non-linear activation function. Next, we add two convolutional block attention module. Under the same experimental conditions, improved OSNet achieves significantly superior accuracy than the original OSNet, maintaining the same speed and compact storage.

Citation Download Citation

Starasotnikau Uladzislau and Xin Feng "Modified omni-scale net architecture for cattle identification on their muzzle point image pattern characteristics", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264520 (23 May 2023); https://doi.org/10.1117/12.2681201

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KEYWORDS

Convolution

Education and training

Animals

Deep learning

Biometrics

Computer vision technology

Data modeling

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