AI decrypts pet emotions: How EfficientNetB5 can help you understand the mood of cats and dogsAI

Have you ever wondered how your pet is feeling at this moment? Is it happy, anxious, or lonely? Traditionally, we can only guess emotions by observing their body language and behavior, but this method is often subjective and inaccurate enough. Nowadays, artificial intelligence technology has brought us a new perspective. The latest research has developed a deep learning-based system that can automatically identify the emotional state of cats and dogs, with an accuracy rate of up to 91.24%. This not only allows pet owners to better understand pets, but also provides strong support for veterinary diagnosis and treatment and animal welfare.

The core advantages of EfficientNetB5 architecture

EfficientNetB5 is an advanced convolutional neural network (CNN) architecture, which is designed to reduce computing resource consumption while ensuring high accuracy. Compared with the traditional model, it can achieve better performance with fewer parameters and calculations.

In this study of pet emotion recognition, the team customized and improved the standard EfficientNetB5 to make it more suitable for capturing subtle changes in the face and posture of animals. The model not only focuses on the overall characteristics, but also recognizes details, such as the angle of the ears, the changes in the eyes and the shape of the mouth.

Key technological innovation

The research team integrated two important components into the model:

Squeeze-and-Excitation (SE) module

This module is similar to the "attention mechanism", which can automatically identify which visual characteristics are the most critical to emotional judgment. It strengthens important information such as ear position, eyes and facial expressions, while suppressing background interference, so that the model can focus on the most valuable clues.

Dense Residual Block

This module enhances the ability of the model to learn complex features and prevents the problem of gradient disappearance during training. Through residual connection, the model can better capture the nuances of pet expressions and postures, thus improving the accuracy of emotional recognition.

Identify four core emotions

The improved EfficientNetB5 model performs well in multiple indicators:

These data show that the model is not only highly accurate, but also performs well among different categories, reducing misjudgment.

Comparison with other models

The research team compared the improved version of EfficientNetB5 with a variety of mainstream deep learning architectures:

ResNet-50: Test accuracy rate 89.10% (second place)

VGG-16: Test accuracy rate 73.52%

MobileNet: Test accuracy rate 52.12%

Inception V3: Test accuracy 41.23%

DenseNet: Test accuracy rate 58.30%

EfficientNetB5 leads with a test accuracy of 91.24%, proving its advantage in pet emotion recognition tasks.

Practical application scenarios

This technology is not only suitable for the daily use of pet owners, but also can play a role in the following areas:

Veterinary diagnosis and treatment

By accurately identifying the emotions of pets, veterinarians can better judge the health status and psychological stress of pets and formulate more appropriate treatment plans.

Animal welfare monitoring

Animal protection agencies can use this system to monitor the emotional changes of pets in shelters, find abnormalities in time, and improve the breeding environment.

Smart pet equipment

In the future, intelligent cameras and pet robots can integrate this technology to realize real-time emotional monitoring and interaction, and improve the life experience of pets.

Future outlook

With the continuous progress of deep learning technology, the pet emotion recognition system will become more accurate and diversified. It may be realized in the future:

Identify more sub-emotions, such as anxiety, curiosity, fear, etc.

Support more pet species, such as rabbits, birds, etc.

Integrate voice and behavior analysis to realize multi-modal emotional recognition

These progresses will help us have a deeper understanding of the inner world of pets and promote the harmonious development of the relationship between human pets. Ref.

《A Deep Learning-Based Approach for Precise Emotion Recognition in Domestic Animals Using EfficientNetB5 Architecture》