对比多种神经网络识别猫个体，发现迁移学习表现更优。
The Comparison of Individual Cat Recognition Using Neural Networks
- 用迁移学习训练传统CNN识别猫
- ConvNeXt和DenseNet效果显著可优化
- 适合宠物店与野外猫群管理
基于深度学习的面部识别已广泛应用于身份认证、智能门锁、照片分组等场景。近年来，诸多网络如ResNet、DenseNet、EfficientNet、ConvNeXt和孪生网络被用于计算机视觉任务。然而，针对猫个体识别，少有研究系统比较不同神经网络的优劣。本研究通过系统比较不同神经网络在猫识别中的表现，发现采用迁移学习训练的传统CNN优于微调方法或孪生网络。此外，ConvNeXt和DenseNet表现优异，具有进一步优化潜力，适用于宠物店及野外猫群管理。
原文摘要 · Abstract (English)
Facial recognition using deep learning has been widely used in social life for applications such as authentication, smart door locks, and photo grouping, etc. More and more networks have been developed to facilitate computer vision tasks, such as ResNet, DenseNet, EfficientNet, ConvNeXt, and Siamese networks. However, few studies have systematically compared the advantages and disadvantages of such neural networks in identifying individuals from images, especially for pet animals like cats. In the present study, by systematically comparing the efficacy of different neural networks in cat recognition, we found traditional CNNs trained with transfer learning have better performance than models trained with the fine-tuning method or Siamese networks in individual cat recognition. In addition, ConvNeXt and DenseNet yield significant results which could be further optimized for individual cat recognition in pet stores and in the wild. These results provide a method to improve cat management in pet stores and monitoring of cats in the wild.
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