摘要
提出了一种用于空中红外目标检测的增强单发多框检测器(SSD)方法。分析了感受野与特征图层数的关系,同时采用池化和转置卷积操作的特征图双向融合机制,从整体上增强了特征的表达能力。通过引入浅层特征图的语义增强分支,并在高分辨率特征图上增加预测框,可提升小尺寸目标的定位精度。在VOC2007小目标和空中红外目标数据集上进行了对比测试,平均精度分别提高了7.1%和8.7%,此时检测速度略有下降。结果表明,增强SSD可在空中红外目标检测中获得较好的性能。
A method for enhancement of a single shot multibox detector(SSD) for aerial infrared target detection is proposed. Herein, the relationship between the sensing field and number of feature layers is analyzed, and a bidirectional feature map fusion mechanism that uses both pooling and deconvolution operations is proposed to enhance the feature expression ability. The semantic enhancement branch of the shallow feature map is introduced and the prediction boxes on the high-resolution feature map are increased, so that the positing accuracy of small-size targets is improved. Comparative experiments on the VOC2007 small object dataset and an aerial infrared target dataset reveal that the mean average precisions increase by 7.1% and 8.7%, respectively, accompanied by a slight decrease in detection speed. The results demonstrate that SSD enhancements can achieve good performance in aerial infrared target detection.
作者
谢江荣
李范鸣
卫红
李冰
邵保泰
Xie Jiangrong;Li Fanming;Wei Hong;Li Bing;Shao Baotai(Shanghai Institute of Technical Physics t Chinese Academy of Sciences f Shanghai 200083,China;University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Infrared System Detection and Imaging Technology,Chinese Academy of Sciences,Shanghai 200083,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2019年第6期215-223,共9页
Acta Optica Sinica
基金
国家十三五国防预研项目(Jzx2016-0404/Y72-2)
上海市现场物证重点实验室基金(2017xcwzk08)
关键词
机器视觉
单发多框检测器
空中红外目标
目标检测
特征融合
语义分割
machine vision
single shot multibox detector
aerial infrared target
target detection
feature fusion
semantic segmentation