Semantic Segmentation for Pet Detection Using Convolutional Neural Network

• The project's title was expanded to provide a more comprehensive and detailed representation.

This research project encompasses an in-depth exploration of semantic segmentation methods for pet detection using the Oxford Pets Dataset. The primary objective involves the developmentof a convolutional neural network model rooted in deep-learning principles, designed to achieve precise segmentation and detection of pets within images. The approach integrates advanced image processing techniques, leveraging deep learning methodologies, and dataset augmentation strategies to enhance pet detection accuracy substantially. The outcomes underscore the considerable potential of semantic segmentation in elevating the effectiveness of pet detection applications. This study offers promising avenues for practical integration in real-world contexts such as pet care and surveillance systems. The achieved advancements underscore the proposed technique's viability and contribute to the broader discourse on enhancing object detection through sophisticated segmentation strategies.