Small object detection model for remote sensing images combining super-resolution assisted reasoning and dynamic feature fusion

Abstract

We provide an innovative methodology for detecting small objects in remote sensing imagery. Our method addresses challenges related to missed and false detections caused by the limited pixel representation of small objects. It integrates super-resolution technology with dynamic feature fusion to enhance detection accuracy. We introduce a cross-stage local feature fusion module to improve feature extraction. In addition, we propose a super-resolution network with soft thresholding to refine small object features, resulting in improving resolution of feature maps while reducing redundancy. Furthermore, we embed a dynamic fusion module based on feature space relationships into a dual-branch network to strengthen the role of the super-resolution branch. Experimental validation on DIOR and NWPU VHR-10 datasets shows mAP improvements to 73.9% and 93.7%, respectively, with FLOPs of 24.89G and 22.33G. Our method outperforms existing approaches regarding accuracy and number of parameters, effectively addressing challenges in small object detection in remote sensing imagery.