液晶与显示2026，Vol.41Issue(2)：253-266,14.DOI:10.37188/CJLCD.2025-0250
面向无人机影像小目标检测的轻量化算法
Lightweight algorithm for small object detection in UAV images
摘要
Abstract
To address the insufficient feature extraction of existing detection models for tiny targets,this paper proposes a lightweight small object detection model named PRSU-YOLO.First,a Pinwheel-shaped Convolutional Adaptive Module is designed to enhance the directional extraction capability of subtle features.Second,a Reparameterized Spatial-Channel Convolution Module is constructed to optimize multi-scale representation through dynamic feature reconstruction.Third,a Small Object Detection Branch is embedded in the neck network to establish an enhancement pathway for high-resolution detail features.Finally,a Scale-based Dynamic Intersection over Union loss function is introduced,enabling the model to adaptively adjust the bounding-box regression strategy.With only a 14.6 GFLOPS increase in computational complexity,the proposed model achieves an mAP@0.5 of 37.4%on the VisDrone2019,representing a significant improvement of 4.4%over the baseline.On the TinyPerson,it attained a precision of 34.5%,which is an increase of 3.5%compared to the baseline.The experimental results demonstrate that the model significantly enhances detection capability while effectively controlling computational cost,providing an effective solution for small object detection tasks in UAV-based ground observation scenarios.关键词
计算机视觉/无人机影像/小目标检测/特征增强Key words
computer vision/unmanned aerial vehicle imagery/small object detection/feature enhancement分类
信息技术与安全科学引用本文复制引用
罗可心,李松江,王鹏,杨华民..面向无人机影像小目标检测的轻量化算法[J].液晶与显示,2026,41(2):253-266,14.基金项目
吉林省科技创新平台建设项目(No.YDZJ202302CXJD027)Supported by Jilin Province's Science and Technology Innovation Platform Construction Project(No.YDZJ202302CXJD027) （No.YDZJ202302CXJD027）
