Small target drone algorithm in low-altitude complex urban scenarios based on ESMS-YOLOv7

被引：0

作者：

Wei, Yuntao ^{[1
]}

Wang, Xiujia ^{[1
]}

Bo, Chunjuan ^{[1
]}

Shi, Zhan ^{[1
]}

机构：

[1] School of information and communication engineering, Dalian Minzu University, Dalian

来源：

Cognitive Robotics | 2025年 / 5卷

基金：

中国国家自然科学基金;

关键词：

Anti-UAV Detection; Complex Scene; Object Detection; Small Target;

D O I：

10.1016/j.cogr.2024.11.004

中图分类号：

学科分类号：

摘要：

The increasing use and militarization of UAV technology presents significant challenges to nations and societies. Notably, there is a deficit in anti- UAV technologies for civilian use, particularly in complex urban environments at low altitudes. This paper proposes the ESMS-YOLOv7 algorithm, which is specifically engineered to detect small target UAVs in such challenging urban landscapes. The algorithm focuses on the extraction of features from small target UAVs in urban contexts. Enhancements to YOLOv7 include the integration of the ELAN-C module, the SimSPPFCSPC-R module, and the MP-CBAM module, which collectively improve the network's ability to extract features and focus on small target UAVs. Additionally, the SIOU loss function is employed to increase the model's robustness. The effectiveness of the ESMS-YOLOv7 algorithm is validated through its performance on the DUT Anti-UAV dataset, where it exhibits superior capabilities relative to other leading algorithms. © 2024

引用

页码：14 / 25

页数：11