红外技术2025，Vol.47Issue(12)：1491-1501,11.
基于RSC-YOLO的无人机目标检测算法
UAV Object Detection Algorithm Based on RSC-YOLO
摘要
Abstract
An improved algorithm called RSC-YOLO based on YOLOv11n is proposed to address the problems of complex backgrounds,dense objects,and mutual occlusion in object detection based on uncrewed aerial vehicle(UAV)platforms.Firstly,the representative feature alignment(RFA)sensing field attention mechanism is introduced,and the Conv and C3k2 models are re-designed,which effectively solves the parameter sharing problem caused by the overlapping of spatial features.Secondly,a shallow detail fusion module(SDFM)is designed for the neck network of YOLO,which emphasizes the attention to shallow features,compensates for the missing features of small objects as well as maintains the integrity of the remaining spatial information of occluded objects.Finally,a lightweight cross-scale feature fusion network CCFF is introduced for the neck,which enhanced the model's feature interaction capability and multi-scale feature fusion capability to achieve a balance between accuracy and detection speed.Ablation and comparison experiments were conducted on the Visdrone2019 dataset,and the mAP50 was improved by 4.5％compared with the baseline algorithm.The framerate of the system reached 213 FPS,which is sufficient for real-time detection.Generalization experiments were conducted on the DOTA dataset and the HIT-UAV infrared small object dataset,and the results show that the mAP50 was improved by 3.1％and 3.9％,respectively,which demonstrates that the proposed algorithm has wide applicability.关键词
基于无人机平台的目标检测/YOLOv11n/感受野注意力机制/浅层细节融合/轻量级特征融合/红外目标检测Key words
object detection based on UAV platforms/YOLOv11n/sensory field attention mechanism/shallow detail fusion/lightweight feature fusion/infrared object detection分类
信息技术与安全科学引用本文复制引用
LIANG Xiuman,ZHANG Yongsheng,WU Nan,YU Haifeng,ZHANG Zhuo..基于RSC-YOLO的无人机目标检测算法[J].红外技术,2025,47(12):1491-1501,11.基金项目
河北省自然科学基金资助(D2024209006),河北省教育厅科学研究项目资助(QN2024147),华北理工大学研究生创新项目(2026S18). （D2024209006）
