Article(id=1262079402100252865, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, articleNumber=null, orderNo=null, doi=10.19651/j.cnki.emt.2519396, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752768000000, receivedDateStr=2025-07-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778832777762, onlineDateStr=2026-05-15, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778832777762, onlineIssueDateStr=2026-05-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778832777762, creator=13701087609, updateTime=1778832777762, updator=13701087609, issue=Issue{id=1262079396291141802, tenantId=1146029695717560320, journalId=1260987750510510108, year='2026', volume='49', issue='6', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778832776377, creator=13701087609, updateTime=1778832914473, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1262079975688122904, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1262079975688122905, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=98, endPage=109, ext={EN=ArticleExt(id=1262079405719937245, articleId=1262079402100252865, tenantId=1146029695717560320, journalId=1260987750510510108, language=EN, title=Small object detection algorithm in Drone aerial images based on RT-DETR, columnId=1262079399856300214, journalTitle=Electronic Measurement Technology, columnName=Theory and Algorithms, runingTitle=null, highlight=null, articleAbstract=

With the continuous expansion of drone application scenarios, small object detection in aerial images has become a research hotspot in the field of computer vision. In view of the problems that small object features are not obvious, complex backgrounds lead to false detection and missed detection, and the existing algorithms are difficult to balance detection accuracy and real-time performance, this paper proposes an aerial image small object detection algorithm FST-RTDETR based on RT-DETR to solve these problems. First, FasterNet is combined with the EMA attention mechanism, and the structure of the Basic Block module of the original module is redesigned to improve the network operation speed and the accuracy of visual tasks. Secondly, in order to solve the problems of excessive calculation and more time-consuming post-processing after adding the traditional P2 detection layer, this study propose to use the P2 feature layer based on the original CCFM architecture to obtain features rich in small object information through SPDConv and give them to P3 for fusion, and then use the CSP idea and Omni-Kernel to improve CSP-OmniKernel for feature integration, effectively learn the feature performance from global to local, and finally reduce the missed detection rate, false detection rate and improve the detection performance of small objects. Finally, in order to simplify the loss function calculation process, improve regression efficiency and accuracy, and have a more comprehensive loss consideration, this study use inner-MPDIoU to replace the original GIoU. Experiments on the improved algorithm on the VisDrone2019 dataset show that the FST-RTDETR model achieves a detection accuracy of 49.6%, which is 2.1% higher than the original RT-DETR model. The FST-RTDETR model significantly improves the object detection performance of drone images, improves model efficiency, and shows good performance compared to other algorithms.

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随着无人机应用场景不断拓展，航拍图像中小目标检测成为计算机视觉领域的研究热点。针对小目标特征不明显、背景复杂导致误检和漏检，现有算法检测精度与实时性难以兼顾等问题，本研究提出了一种基于RT-DETR的航拍图像小目标检测算法FST-RTDETR来解决这些问题。首先，将FasterNet与EMA注意力机制结合，重新设计原有模块的Basic Block模块的结构，实现提高网络运行速度和视觉任务的准确性。其次，为了解决传统P2检测层添加后出现计算量过大、后处理更加耗时等问题，本研究基于原本的CCFM架构上提出使用P2特征层经过SPDConv得到富含小目标信息的特征给到P3进行融合，然后使用CSP思想和基于Omni-Kernel进行改进得到CSP-OmniKernel进行特征整合，有效地学习从全局到局部的特征表现，最终减少漏检率、误检率和提高小目标的检测性能。最后，为了使得模型简化损失函数计算过程、改进回归效率和精度以及拥有更全面的损失考虑，使用inner-MPDIoU替换原来的GIoU。改进后的算法在VisDrone2019数据集上的实验表明，FST-RTDETR模型实现了49.6%的mAP@50，相对于原来的RT-DETR模型提高了2.1%。FST-RTDETR模型显著提升了无人机图像的目标检测性能，提高了模型效率，对比其他算法表现出了良好的性能。

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刘杰，硕士研究生，主要研究方向为深度学习、图像处理，目标检测。E-mail: 17785100921@163.com

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刘杰，硕士研究生，主要研究方向为深度学习、图像处理，目标检测。E-mail: 17785100921@163.com

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李志文，硕士研究生，主要研究方向为深度学习、目标检测，空间域知识图谱。E-mail: 79614027@qq.com

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李志文，硕士研究生，主要研究方向为深度学习、目标检测，空间域知识图谱。E-mail: 79614027@qq.com

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张腾庆，硕士研究生，主要研究方向为深度学习、目标检测。E-mail: 1484315072@qq.com

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张腾庆，硕士研究生，主要研究方向为深度学习、目标检测。E-mail: 1484315072@qq.com

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