The paper introduces the first large-scale dataset, ICRWE, for cattle face recognition in wild environments. The dataset covers diverse lighting conditions and face orientations, addressing the challenges of applying existing algorithms designed for captive environments to real-world wild settings.
The authors propose a novel parallel attention network, PANet, which comprises multiple cascaded Transformer modules. Each module incorporates two parallel components: Position Attention Modules (PAM) and Feature Mapping Modules (FMM). PAM focuses on local and global features at each image position through parallel channel attention, while FMM captures intricate feature patterns through non-linear mappings. The parallel structure and attention mechanisms enable PANet to effectively extract robust features for cattle recognition.
Experiments on the ICRWE dataset demonstrate that PANet outperforms state-of-the-art methods, achieving an accuracy of 88.03%. The authors also conduct ablation studies to validate the effectiveness of the parallel structure and attention modules. Furthermore, they show that background removal through face detection significantly improves the recognition performance across various models.
The introduction of the ICRWE dataset and the development of the PANet model represent significant contributions to the field of cattle recognition, addressing the challenges of real-world wild environments and paving the way for practical applications in the livestock industry.
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by Jiayu Li,Xue... at arxiv.org 04-01-2024
https://arxiv.org/pdf/2403.19980.pdfDeeper Inquiries