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谭红臣, 李淑华, 刘彬, 刘秀平. 特征增强的SSD算法及其在目标检测中的应用[J]. 计算机辅助设计与图形学学报, 2019, 31(4): 573-579. DOI: 10.3724/SP.J.1089.2019.17331
引用本文: 谭红臣, 李淑华, 刘彬, 刘秀平. 特征增强的SSD算法及其在目标检测中的应用[J]. 计算机辅助设计与图形学学报, 2019, 31(4): 573-579. DOI: 10.3724/SP.J.1089.2019.17331
Tan Hongchen, Li Shuhua, Liu Bin, Liu Xiuping. Feature Enhancement SSD for Object Detection[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(4): 573-579. DOI: 10.3724/SP.J.1089.2019.17331
Citation: Tan Hongchen, Li Shuhua, Liu Bin, Liu Xiuping. Feature Enhancement SSD for Object Detection[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(4): 573-579. DOI: 10.3724/SP.J.1089.2019.17331

特征增强的SSD算法及其在目标检测中的应用

Feature Enhancement SSD for Object Detection

  • 摘要: 针对多尺度单发射击检测(SSD)算法不同尺度的特征层很难进行融合互补问题,提出一种特征增强的SSD(FE-SSD)算法.首先对SSD算法的金字塔特征层中,每一尺度的特征进行尺寸不变的卷积操作;然后将卷积前与卷积后的特征进行特征融合操作,进而产生一组新的金字塔特征层;最后在新产生的金字塔特征层上执行目标的检测与定位任务.在PASCALVOC2007公共数据库上进行实验,当输入图像尺寸为300×300时,检测精度(mAP)达到78.0%,检测速度(FPS)达到82.5帧/s.此外,在拓展实验中,文中算法对图像中模糊目标的检测效果也优于SSD算法.

     

    Abstract: This paper presents feature enhancement single shot multi-box detector(FE-SSD) for object detection. In FE-SSD network structure, firstly we apply scale-invariant convolution operation on each scale feature map in SSD’s pyramid feature maps. Then fusing the original feature and convolved feature generates new SSD’s feature pyramid, which will be fed to multibox detectors to predict the final detection results.On the PASCAL VOC2007 test, our network can achieve 78.0% mean average precision(mAP) at the speed of 82.5 frame per second(FPS) with the input size 300×300. On extended experiment, FE-SSD performance over SSD in blurry object detection.

     

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