计算机与数字工程2024，Vol.52Issue(10)：2972-2976,5.DOI:10.3969/j.issn.1672-9722.2024.10.021
基于分步多尺度特征融合的SSD目标检测算法
SSD Target Detection Algorithm Based on Stepwise Multi-scale Feature fusion
摘要
Abstract
In order to solve the problem that SSD(single shot multibox detector)target detection algorithm has poor detection precision for small targets due to weak shallow feature representation ability,an SSD target detection algorithm based on stepwise multi-scale feature fusion is proposed.In order to increase the details and semantic information contained in the shallow features of SSD model,two feature layer are introduced in the low-level feature part of SSD model.Only the two feature maps of the lower layer of the model are deconvolutioned,and the feature maps of three different scales of the lower layer are fused in two steps,which not only improves the representation ability of the shallow feature of the model,but also reduces the calculation amount in the running process of the algorithm.Experimental results show that,on PASCAL VOC2007 dataset,the AP value of small target category is greatly improved by the improved algorithm,and the mAP value is 3.6%higher than that of SSD algorithm.The detection speed of the algorithm also meets the real-time requirement.关键词
目标检测/SSD/反卷积/分步多尺度特征融合Key words
target detection/single shot multibox detector/deconvolution/stepwise multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
蒋帅,薛波..基于分步多尺度特征融合的SSD目标检测算法[J].计算机与数字工程,2024,52(10):2972-2976,5.基金项目
江苏理工学院研究生实践创新计划(编号:XSJCX21_27)资助. （编号:XSJCX21_27）
