华南理工大学学报（自然科学版）2026，Vol.54Issue(6)：193-204,12.DOI:10.12141/j.issn.1000-565X.250327
低空视角下改进无人机小目标检测算法
Research on Improved Small-Object Detection Algorithm for UAVs from Low-Altitude Perspectives
摘要
Abstract
In the context of the rapidly growing low-altitude economyand the urgent need for high-precision,lightweight UAV object detection in agriculture,logistics,and emergency rescue,this paper tackles the challenges of low target pixel occupancy,environmental occlusion,and severe perspective distortion in low-altitude imagery.Based on the RT-DETR algorithm,an enhanced detection model—Cross-scale Alignment and Position Encoding Enhanced RT-DETR(CAPE-RT-DETR)—is proposed.Firstly,to overcome the limitation of traditional static convolution kernels in terms of feature extraction flexibility under complex backgrounds,this paper proposes a feature enhancement moduleintegrating dynamic convolution kernel generation and gated feature selection,termed C2ML.By utilizing a Large Kernel Predictor(LKP)to dynamically generate spatially adaptive convolution kernels,and combining it with a gated feature selection mechanism to eliminate redundant background information,the module significantly enhances the model's ability to extract and filter critical features.Secondly,to address the geometric distortion and spatially non-uniform deformation characteristic of aerial perspectives,a learnable positional encoding is integrated with the multi-head self-attention mechanism to construct an enhanced position-aware interaction module,termed AIFP.By learning spatial prior information in an end-to-end manner,this module effectively improves the model's perceptual sensitivity and localization accuracy with respect to the low-altitude-specific spatial structures.Finally,to resolve the pixel misalignment problem caused by simple upsampling in multi-scale feature fusion,a a cross-scale feature calibration(CSFC)module is introduced.This module utilizes a pyramid scene parsing structure to integrate sparse global context and employs a dual-path convolution and grid sampling mechanism to explicitly compensate for cross-scale alignment biases,thereby achieving consistent representation of semantic information.Experimental results on the ALU and VisDrone2019 datasets demonstrate that CAPE-RT-DETR outperforms the baseline algorithm in terms of parameter count,accuracy,and model size.Meanwhile,ablation experiments validate the effectiveness and synergy of the three improved modules.This research provides a high-precision and lightweight methodological foundation and theoretical support for real-time UAV object detection in complex scenarios.关键词
低空交通/目标检测/无人机/RT-DETRKey words
low-altitude traffic/object detection/unmanned aerial vehicle/RT-DETR分类
交通工程引用本文复制引用
张杰,董春彤,裴玉龙,何庆龄..低空视角下改进无人机小目标检测算法[J].华南理工大学学报（自然科学版）,2026,54(6):193-204,12.基金项目
国家自然科学基金重点项目(51638004) （51638004）
福建省自然科学基金项目(2023J011093) （2023J011093）
东北林业大学中央高校基本科研业务费专项资金项目(2572023CT21-02).Supported by the the Key Project of National Natural Science Foundation of China(51638004)and the Natural Science Foundation of Fujian Province(2023J011093) （2572023CT21-02）
