Lightweight Small-Object Detection for Remote Sensing Images Integrating Super-Resolution and Feature Enhancement

Abstract

To address the problems of low pixel size, complex background, and limited hardware resources in remote sensing image object detection, a small-object detection algorithm that combines Super-Resolution(SR) and feature enhancement is proposed. The Ghost convolution layers in the GhostNet are used to replace the conventional convolution layers, Conv, in the You Only Look Once v8 (YOLOv8) network, reducing the number of parameters and calculations of the network model without compromising detection accuracy. A Super-Resolution Assisted Enhancement (SRAE) is built in the backbone network to improve image resolution and feature extraction capabilities. A Three-layer Feature Fusion (TFF) module is proposed to obtain the spatial features of the lower layer of the backbone network, improve the insufficient feature space extraction in the Spatial Pyramid Pooling Fast(SPPF) layer, and enhance the spatial positioning ability of small targets. A Self-Attention information Transfer (SAT) module is designed to enhance the semantic and global information of small targets while ensuring a lightweight model. The improved model achieves 90.5% Mean Average Precision (mAP)@0.5, 15.1×10⁶ parameter quantity, and 30.3×10⁹ Floating Point Operations Per Second (FLOPs) on the DIOR dataset; additionally, it achieves lightweight while improving detection accuracy compared to other models.