@Article{cmc.2025.059856,
AUTHOR = {Lijuan Zhang, Xiaoyu Wang, Songtao Zhang, Yutong Jiang, Dongming Li, Weichen Sun},
TITLE = {Target Detection-Oriented RGCN Inference Enhancement Method},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {1},
PAGES = {1219--1237},
URL = {http://www.techscience.com/cmc/v83n1/60081},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2025.059856}
}