Machine learning techniques for supporting dog grooming services

Abstract

In recent years, there has been a remarkable surge in the popularity of dog grooming, which has resulted in a growing interest in leveraging cutting-edge technologies to streamline the process and enhance the overall experience. Specifically, computer vision and machine learning techniques have been garnering increasing attention as a means to assist dog groomers in classifying dog breeds. This paper explores the use of machine learning techniques based on Convolutional Neural Networks (CNNs) for classifying dog breeds and estimating the time required for bathing and grooming each dog. The study involves collecting a large dataset of images and corresponding grooming information for a diverse set of dog breeds. The effectiveness of the proposed method is evaluated using a range of performance metrics, including accuracy, precision, recall, and F1 score. Our study suggests that proposed CNNs can be valuable in helping dog owners and groomers identify the correct breed of a dog and estimate the grooming time before receiving the service. The accuracy of classification obtained by the proposed method achieves a 19% increase compared with other recently developed techniques. Finally, this work contributes to the development of a user-friendly application that allows customers to book dog grooming services, providing predictions for dog breeds and estimated grooming time.