Multi-Scale Small Object Detection in Satellite Images using Vision Transformers

Banduku Ramesh; A N Dinesh Kumar

Multi-Scale Small Object Detection in Satellite Images using Vision Transformers

Authors: Banduku Ramesh, A N Dinesh Kumar

Unique Paper ID: 175770
Volume: 11
Issue: 11
PageNo: 4013-4019

Keywords: No Keywords Found

Abstract:
The Object-Centric Masked Image Modeling (OCMIM)-based Self-Supervised Pre-training (SSP) method has revolutionized remote sensing object detection. Traditional SSP models struggle to detect small-scale objects due to their reliance on scene-level representations. OCMIM introduces an object-centric data generator and an attention-guided mask generator to enhance object-level representation learning. The proposed work extends this model by integrating advanced pre-trained architectures such as VGG16, improving detection accuracy. By reconstructing masked object regions using attention-based techniques, the system enhances remote sensing imagery analysis. Our results show that the extended approach significantly outperforms previous methodologies in precision, recall, and overall detection performance.

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ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4013-4019

Multi-Scale Small Object Detection in Satellite Images using Vision Transformers

Available:https://ijirt.org/Article?manuscript=175770

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