Drone-Based High-Precision Object Detection in Remote Sensing with Attention-Guided Feature Fusion

Small object detection in remote sensing imagery is a challenging task due to the small size of targets, complex background, and low contrast, which makes achieving high precision difficult. To enhance the accuracy of detection, this study proposes a novel oriented object detection model with three significant innovations: Firstly, a lightweight feature extraction network is designed to achieve efficient feature representation at a reduced computational cost, which is particularly effective for the recognition of small targets in remote sensing imagery. Secondly, a Feature-Focused Channel Attention (FFCA) is introduced that enhances the model’s ability to focus on small target areas by combining spatial and channel attention, enhancing the model’s capacity to capture and represent features more effectively. Lastly, an attention-guided multi-scale feature fusion module is proposed to integrate features from different levels, which substantially boosts the model’s ability to accurately detect small-scale objects, especially in remote sensing scenarios with vast fields of view and complex backgrounds. The experimental outcomes validate that our model achieves the best detection performance on two benchmark public datasets for remote sensing imagery, confirming its effectiveness and practicality in remote small object detection tasks.