Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 78, Issue 1, Pages 913-927Publisher
SPRINGER
DOI: 10.1007/s11042-018-5870-3
Keywords
Feature pyramid; Object detection; Convolutional neural network; Multi-scale detection; Deep learning
Categories
Funding
- Shanxi Science Foundation [2015011045]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available