西安电子科技大学学报（自然科学版）2025，Vol.52Issue(2)：47-56,10.DOI:10.19665/j.issn1001-2400.20250304
一种轻量级小目标无人机检测YOLO模型
Lightweight YOLO model for small UAV object detection
摘要
Abstract
Due to the small size of Unmanned Aerial Vehicles(UAVs),complex airspace background,and easy confusion with sky objects such as birds,the existing object detection models lack sufficient accuracy.Although increasing the model size can improve the detection accuracy to a certain extent,it also reduces the inferring speed and significantly increases the number of parameters and computational complexity of the model.In addition,the lack of datasets which are suitable for small UAV object detection makes it challenging to provide adequate support for designing effective models.To address the aforementioned deficiencies,this paper first constructs a dataset from existing open-source datasets using a target-area-compression based small object sample enhancement method,which can be utilized in small UAV object detection tasks.Then,we design a lightweight and high-accuracy network model called YOLO-LADC,based on the YOLOv8.This model incorporates a novel downsampling convolution structure that reduces the number of model parameters and computations while enhancing the detection accuracy.Moreover,we add a small object detection branch to the neck network of the YOLO-LADC to achieve the YOLO-LADCS,which is better suited for small UAV object detection tasks.Comparative experiments show that the YOLO-LADCS is able to improve the average accuracy of a small object by 1.1％with a 14％reduction in the number of parameters compared to the YOLOv8n(a lightweight version of the YOLOv8).关键词
目标检测/神经网络/无人机检测/小目标/轻量化Key words
object detection/neural networks/uav detection/small object/lightweight分类
计算机与自动化引用本文复制引用
阳小兵,李钊,许艳红..一种轻量级小目标无人机检测YOLO模型[J].西安电子科技大学学报（自然科学版）,2025,52(2):47-56,10.基金项目
国家自然科学基金(62072351,62202359,U23A20300) （62072351,62202359,U23A20300）
陕西省重点研发计划(2023JCZD35) （2023JCZD35）
高等学校学科创新引智计划(B16037) （B16037）
河南省科技攻关项目(252102211120) （252102211120）
