计算机工程与应用2024，Vol.60Issue(20)：198-206,9.DOI:10.3778/j.issn.1002-8331.2404-0114
面向无人机小目标的RTDETR改进检测算法
Improved Detection Algorithm of RTDETR for UAV Small Target
摘要
Abstract
An improved RTDETR detector is proposed to solve the challenges in UAV target detection,such as small and dense targets,complex background and limited hardware conditions.In the backbone network,lightweight multi-scale attention feature extraction module(Rep-FasterNet EMA block)is designed,and RepConv is used to improve the FasterNet block.Meanwhile,multi-scale attention module(EMA)is introduced to enhance the spatial feature extraction capability and reduce the computational redundancy.In the Encoder part,DyASF feature fusion structure is used to replace CCFM,and dynamic scale sequence feature fusion(DySSFF)module and triple feature encoder(TPE)module are used to avoid the loss of small target feature information caused by up and down sampling,enrich the detailed information of small tar-get detection,and enhance the network scale feature fusion capability.Finally,for the loss function,combining the advan-tages of Focaler-IoU and shape-IoU,Focaler-Shape-IoU is proposed to replace the original model GIOU,inject the shape and scale information of the bounding box,focus the difficult samples,and enhance the bounding box regression effect.The experimental results show that the mAP0.5 and mAP0.5:0.95 of the improved model on the Visdrone2019 dataset are improved by 1.6 percentage points and 0.7 percentage points respectively,while the weight file size has been reduced to a certain extent,which verifies the effectiveness of the improved model.关键词
无人机遥感/小目标检测/RTDETR/多尺度注意力Key words
UAV remote sensing/small target detection/real-time detection Transformer(RTDETR)/multiscale attention分类
信息技术与安全科学引用本文复制引用
胡佳乐,周敏,申飞..面向无人机小目标的RTDETR改进检测算法[J].计算机工程与应用,2024,60(20):198-206,9.基金项目
国家自然科学基金(51975431). （51975431）
