Deep Learning to Extract Animal Images With the U-Net Model on the Use of Pet Images

 (*)Agus Perdana Windarto Mail (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
 Indra Riyana Rahadjeng (Universitas Bina Sarana Informatika, Jakarta, Indonesia)
 Muhammad Noor Hasan Siregar (Universitas Graha Nusantara, Padang Sidempuan, Indonesia)
 Putrama Alkhairi (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)

(*) Corresponding Author

Submitted: December 20, 2023; Published: January 28, 2024


This article explores the innovative application of deep learning techniques, specifically the U-Net model, in the realm of computer vision, focusing on the extraction of animal images from diverse pet datasets. As the digital landscape becomes increasingly saturated with pet imagery, the need for precise and efficient image extraction methods becomes paramount. The study delves into the challenges posed by varying animal poses and backgrounds, presenting a comprehensive analysis of the U-Net model's adaptability in handling these complexities. Through rigorous experimentation, this research refines existing methodologies, enhancing the accuracy of animal image extraction. The findings not only contribute to advancing the field of computer vision but also hold significant implications for wildlife monitoring, veterinary diagnostics, and the broader domain of image processing.


Deep Learning; U-Net Model; Animal Image Extraction; Computer Vision; Pet Images; Semantic Segmentation

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