无线电工程2026，Vol.56Issue(1)：30-39,10.DOI:10.3969/j.issn.1003-3106.2026.01.004
YOLOX同步特征融合网络及其遥感目标检测
YOLOX Synchronization Feature Fusion Network and Remote Sensing Object Detection
摘要
Abstract
To address the issues of poor localization and low detection accuracy in optical remote sensing image-based object detection,an improved lightweight object detection model and algorithm based on YOLOX is proposed.In the structural network design,building upon a Biased Texture Feature Puamid Networks(BTFPN),a synchronous feature fusion network is proposed to fully exploit shallow-layer detailed information,focus on important channel features,and efficiently transfer location information and edge features from shallow-layer networks.In the detection head,by combining the advantages of high-resolution detection heads for small object detection with the requirements of the detection head for enhanced bounding box regression tasks,the detection head is improved into a regression-enhanced and feature-enhanced detection head.This modification addresses the problem of missed detections caused by the loss of semantic information in small objects and improves the inference capability of bounding box regression.Additionally,by establishing an improved SIoU loss function that focuses on the distance and shape differences between bounding boxes,the object localization accuracy is improved.Comparative experimental results on the remote sensing datasets DIOR and RSOD show that the proposed model achieves small regression loss with fewer parameters and demonstrates high detection accuracy for objects of various sizes.关键词
YOLOX/注意力机制/目标检测/特征融合/遥感图像Key words
YOLOX/attention mechanism/object detection/feature fusion/remote sensing image分类
信息技术与安全科学引用本文复制引用
范清华,张著洪..YOLOX同步特征融合网络及其遥感目标检测[J].无线电工程,2026,56(1):30-39,10.基金项目
国家自然科学基金(62063002)National Natural Science Foundation of China(62063002) （62063002）
