A novel hybrid deep learning approach for super-resolution and objects detection in remote sensing

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Abstract
Object detection in remote sensing imagery presents challenges due to low resolution, complex backgrounds, occlusions, and scale variations, which are critical in disaster response, environmental monitoring, and surveillance. This study proposes a robust object detection framework integrating super-resolution techniques with advanced feature extraction algorithms for remote sensing images. The hybrid model combines Advanced StyleGAN and Swin Transformer. Advanced StyleGAN enhances image resolution, facilitating the detection of small and occluded objects, while Swin Transformer employs hierarchical attention mechanisms for effective feature extraction. Preprocessing techniques, including data augmentation, are incorporated to improve the diversity and accuracy of the training dataset. Evaluation on datasets such as VEDAI-VISIBLE and VEDAI-IR demonstrated exceptional performance, achieving an mAP@0.5 of 97.2%, mAP@0.5:0.95 of 72.8%, and F1-Score of 0.93, with an inference time of 42 ms. The framework maintained robustness under challenging conditions, such as low light and fog, outperforming YOLOv9-S, YOLOv9-E, and DCNN-based methods. Furthermore, it surpassed state-of-the-art models on RSOD and NWPU VHR-10 datasets, achieving superior detection accuracy and robustness. This framework offers a significant advancement in remote sensing object detection, providing an effective solution for complex scenarios. Future work may focus on optimizing computational efficiency and expanding the framework to multimodal or dynamic object detection tasks.

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