Semantic Segmentation For Pet Detection Using Convolutional Neural Network

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作者

Chibuzo Nwadike¹

1. Illinois Institute of Technology

会议/活动

International Conference on Computer Vision and Virtual Reality 2023, November 2023 (Sanya, China)

海报摘要

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 development of 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.

关键词

Deep Learning, Machine Learning, Computer Vision, Convolutional Neural Network, Image Processing, Semantic Segmentation

研究领域

Computer and Information Science , Electrical Engineering

参考文献

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基金

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补充材料

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附加信息

利益冲突

No competing interests were disclosed.

数据可用性声明

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

知识共享许可协议

Copyright © 2023 Nwadike. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.