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The need to accurately identify dog breeds is important due to their popularity and role as companions. With an increase in the variety of dog breeds worldwide, there is a pressing demand for better classification methods. Advances in artificial intelligence have resulted in the creation of a new model that combines YOLOv7 with the Whale Optimization Algorithm (WOA) in a Convolutional Neural Network (CNN) setup, designed specifically for detecting dog breeds. This model uses the extensive Stanford Dogs dataset, which includes 120 unique breeds, and has been thoroughly tested. It has shown to be more accurate and precise than current methods. The effectiveness of this model in identifying dog breeds marks a significant improvement in the field and suggests its usefulness in various areas such as veterinary services, dog behavior studies, and improved pet management systems. Additionally, this method showcases the benefits of merging advanced machine learning techniques with nature-inspired algorithms to address complex identification challenges, making a significant contribution to the development of AI-based solutions in animal identification and related areas.
Singh et al. (Fri,) studied this question.
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