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

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.