Fine-Grained Feature Enhancement for Object Detection in Remote Sensing Images

Object detection in remote sensing images is a challenging task due to the complex backgrounds, diverse object shapes and sizes, and varying imaging conditions. To address these challenges, fine-grained feature enhancement can be employed to improve object detection accuracy.

Fine-grained feature enhancement is a technique that extracts and enhances features at multiple scales and resolutions to capture fine details of objects. This technique includes two main steps: feature extraction and feature enhancement.

In the feature extraction step, convolutional neural networks (CNNs) are used to extract features from the input image. The extracted features are then fed into a feature enhancement module, which enhances the features by incorporating contextual information and fine-grained details.

The feature enhancement module employs a multi-scale feature fusion technique to combine features at different scales and resolutions. This technique helps to capture fine details of objects and improve the accuracy of object detection.

To evaluate the effectiveness of fine-grained feature enhancement for object detection in remote sensing images, experiments were conducted on two datasets: the NWPU-RESISC45 dataset and the DOTA dataset.

The experimental results demonstrate that fine-grained feature enhancement can significantly improve the accuracy of object detection in remote sensing images. The proposed method outperforms state-of-the-art object detection methods on both datasets.

In conclusion, fine-grained feature enhancement is an effective technique to improve the accuracy of object detection in remote sensing images. This technique can be applied to a wide range of applications, such as urban planning, disaster management, and environmental monitoring.