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Research on improved algorithm of object detection based on feature pyramid.
- Source :
- Multimedia Tools & Applications; Jan2019, Vol. 78 Issue 1, p913-927, 15p
- Publication Year :
- 2019
-
Abstract
- To solve the low detection accuracy of SSD for the small size object, this paper proposed an improved algorithm of SSD object detection based on the feature pyramid (FP-SSD). In the deep convolutional neural network, the high-level features contain well semantic information but are not sensitive to the translations. The low-level features have high resolutions but could not represent the features well. The feature pyramid structure contains multi-scale features. To combine the high and low-level features of the pyramid, the algorithm of this paper applied the deconvolution network to the high-level features of the feature pyramid to get the semantic information, dilated convolution network to learn the position information of the low-level features and used convolution for the middle level features to reduce the feature channels, then used convolution to fuse the features. After using the algorithm, a multi-scale detection structure is constructed. FP-SSD achieves a mean accuracy of 79% on PASCAL VOC2007, and 47% on MSCOCO, which has a great improve compared with SSD. We compared the detection accuracy and results with all kinds of scales by experiments, compared with SSD, the accuracy of FP-SSD is higher, which has more accurate location and higher recognition confidence. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
NEURAL circuitry
DEEP learning
COMPUTER vision
PATTERN perception
Subjects
Details
- Language :
- English
- ISSN :
- 13807501
- Volume :
- 78
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Multimedia Tools & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 134393625
- Full Text :
- https://doi.org/10.1007/s11042-018-5870-3