Target Detection-Oriented RGCN Inference Enhancement Method  

作　　者：Lijuan Zhang Xiaoyu Wang Songtao Zhang Yutong Jiang Dongming Li Weichen Sun 

机构地区：[1]School of Internet of Things Engineering,Wuxi University,Wuxi,214105,China [2]College of Computer Science and Engineering,Changchun University of Technology,Changchun,130012,China [3]Computer Science and Technology at Guohao Academy,Tongji University,Shanghai,200092,China [4]China North Vehicle Research Institute,Beijing,100072,China

出　　处：《Computers, Materials & Continua》2025年第4期1219-1237,共19页计算机、材料和连续体(英文)

基　　金：supported by the National Natural Science Foundation of China(61806024,62206257);the Jilin Province Science and Technology Development Plan Key Research and Development Project(20210204050YY);the Wuxi University Research Start-up Fund for Introduced Talents(2023r004,2023r006);Jiangsu Engineering Research Center of Hyperconvergence Application and Security of IoT Devices,Jiangsu Foreign Expert Workshop,Wuxi City Internet of Vehicles Key Laboratory.

摘　　要：In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield environments.By combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on it.Due to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases significantly.Therefore,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network architecture.Experimental results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target detection.The research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.

关 键 词：RGCN target detection urban battlefield YOLO visual reasoning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]