Research Article

MobileNetV2: Transfer Learning for Elephant Detection

by  Samay Sawal, Shailendra Singh Kathait
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Issue 61
Published: January 2025
Authors: Samay Sawal, Shailendra Singh Kathait
10.5120/ijca2025924403
PDF

Samay Sawal, Shailendra Singh Kathait . MobileNetV2: Transfer Learning for Elephant Detection. International Journal of Computer Applications. 186, 61 (January 2025), 59-65. DOI=10.5120/ijca2025924403

                        @article{ 10.5120/ijca2025924403,
                        author  = { Samay Sawal,Shailendra Singh Kathait },
                        title   = { MobileNetV2: Transfer Learning for Elephant Detection },
                        journal = { International Journal of Computer Applications },
                        year    = { 2025 },
                        volume  = { 186 },
                        number  = { 61 },
                        pages   = { 59-65 },
                        doi     = { 10.5120/ijca2025924403 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2025
                        %A Samay Sawal
                        %A Shailendra Singh Kathait
                        %T MobileNetV2: Transfer Learning for Elephant Detection%T 
                        %J International Journal of Computer Applications
                        %V 186
                        %N 61
                        %P 59-65
                        %R 10.5120/ijca2025924403
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

This study presents a very new and innovative approach to classify the elephants using a deep learning framework [2] which is built on transfer learning and data augmentation techniques. By using the features of MobileNetV2 as the base model, followed by different layers, the model achieved a high accuracy in classifying between images of elephants and other objects. This paper explains in detail about the end-to-end process, including dataset preparation, pre-processing, model architecture, and evaluation metrics. The results indicated the effectiveness of the proposed model in obtaining a high classification accuracy with a robust generalization across the training and validation datasets.

References
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Index Terms
Computer Science
Information Sciences
Elephant Classification
Animals
Forest
Animal Identification
Pattern Recognition
Wildlife Preservation
Keywords

Elephant Classification Deep Learning MobileNetV2 Transfer Learning Data Augmentation

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