학술논문
Object Detection for Chinese Traditional Costume Images Based GRP-DSOD++ Network
Document Type
Chapter
Author
Goos, Gerhard, Founding Editor; Hartmanis, Juris, Founding Editor; Bertino, Elisa, Editorial Board Member; Gao, Wen, Editorial Board Member; Steffen, Bernhard, Editorial Board Member; Woeginger, Gerhard, Editorial Board Member; Yung, Moti, Editorial Board Member; Zhao, Yao, Editor; Barnes, Nick, Editor; Chen, Baoquan, Editor; Westermann, Rüdiger, Editor; Kong, Xiangwei, Editor; Lin, Chunyu, Editor; Zhao, Haiying; Yang, Ting; Hou, Xiaogang; Zhu, Hui; Yang, Zhuoyu
Source
Image and Graphics : 10th International Conference, ICIG 2019, Beijing, China, August 23–25, 2019, Proceedings, Part I. 11/28/2019. 11901:18-31
Subject
Language
English
ISSN
0302-9743
1611-3349
1611-3349
Abstract
The image object detection methods based on deep learning have achieved remarkable results in recent years. However, as object sizes of Chinese Traditional Costume Images (CTCI-4) data set are smaller than that of natural images, and there are not enough training samples, the previous excellent object detection methods cannot achieve good detection result. To tackle this issue, mainly inspired by GRP-DSOD, we propose an effective network, namely GRP-DSOD++ network, to detect objects in the CTCI-4 data set. In order to collect multi-scale context information and capture a wider range of features, we introduce Dilated-Inception module (DI module) and applied it to object detection framework that is learned from scratch. We also applied other advanced components of several excellent object detectors to the proposed network architecture. The proposed detector in the CTCI-4 data set achieves 77.08% mAP, higher than the GRP-DSOD detector (75.33% mAP). And the detector (learning on VOC “07+12” trainval) also can achieve good performance on PASCAL VOC2007.