[1]ZHAO Wenqing,ZHOU Zhendong,ZHAI Yongjie.SSD small target detection algorithm based on deconvolution and feature fusion[J].CAAI Transactions on Intelligent Systems,2020,15(2):310-316.[doi:10.11992/tis.201905035]
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SSD small target detection algorithm based on deconvolution and feature fusion
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 2
Page number:
310-316
Column:
学术论文—机器学习
Public date:
2020-03-05
- Title:
- SSD small target detection algorithm based on deconvolution and feature fusion
- Keywords:
- small target detection; deconvolution; feature mapping; multi-scale; feature fusion; SSD model; PASCAL VOC dataset; KITTI dataset
- CLC:
- TP18
- DOI:
- 10.11992/tis.201905035
- Abstract:
- Given the low resolution and noise of small targets, most target detection algorithms cannot effectively utilize the edge and semantic information of small targets in feature maps, which makes it difficult to distinguish the features from the background. Thus, the detection effect is poor. To solve the problem of insufficient feature information of small and medium targets in the single shot MultiBox detector (SSD) model, we propose a method based on deconvolution and feature fusion. First, deconvolution is employed to process the shallow feature layer to increase the resolution of the feature graph. Then, the feature map of the convolution layer conv11_2 in the SSD model is sampled and spliced. Subsequently, a new layer of features is obtained. Finally, the new layer of features is combined with the feature layer of the four scales inherent in the SSD model. The improved method is compared with the SSD and DSSD methods on the VOC2007 dataset and KITTI vehicle detection dataset. The results show that the method reduced the missed detection rate of small targets and improved the average detection accuracy of all targets.
- References:
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