Deformable Attention Mechanisms Applied to Object Detection, case of Remote Sensing

TL;DR

This paper applies deformable attention-based Transformer models to remote sensing object detection, demonstrating high accuracy on optical and SAR datasets, advancing deep learning methods in geospatial image analysis.

Contribution

It introduces the use of Deformable-DETR architecture for remote sensing object detection across optical and SAR images, showing improved performance over existing models.

Findings

F1 score of 95.12% on optical dataset

F1 score of 94.54% on SAR dataset

Outperforms CNN and standard Transformer models

Abstract

Object detection has recently seen an interesting trend in terms of the most innovative research work, this task being of particular importance in the field of remote sensing, given the consistency of these images in terms of geographical coverage and the objects present. Furthermore, Deep Learning (DL) models, in particular those based on Transformers, are especially relevant for visual computing tasks in general, and target detection in particular. Thus, the present work proposes an application of Deformable-DETR model, a specific architecture using deformable attention mechanisms, on remote sensing images in two different modes, especially optical and Synthetic Aperture Radar (SAR). To achieve this objective, two datasets are used, one optical, which is Pleiades Aircraft dataset, and the other SAR, in particular SAR Ship Detection Dataset (SSDD). The results of a 10-fold stratified validation showed that the proposed model performed particularly well, obtaining an F1 score of 95.12% for the optical dataset and 94.54% for SSDD, while comparing these results with several models detections, especially those based on CNNs and transformers, as well as those specifically designed to detect different object classes in remote sensing images.