@inproceedings{e30cdc15c75946d98ef6032e82374628,
title = "Rich Features and Precise Localization with Region Proposal Network for Object Detection",
abstract = "Deep Network greatly accelerates the development of object detection. Recent advances in object detection are mainly attributed to the combination of deep network and region proposal methods [1–3]. However, the accuracy of object detection on the complicated datasets is still not satisfied, especially on small object detection. This is mainly because of the coarseness of the convolution feature maps. In this paper, we design a new strategy for generating region proposals and propose a new localization method for object detection. Compared with previous baseline detectors such as Fast R-CNN [4] and Faster R-CNN [5], Our method makes use of the adjacent-level feature maps at all scales to generate region proposals and also adopts the cascaded region proposal network (RPN) to fine-tune the location of the bounding box. Compared with other state-of-the-art methods, our method achieves the best recall and object detection accuracy.",
keywords = "Cascaded, Features, Localization, Object detection, Proposal",
author = "Mengdie Chu and Shuai Wu and Yifan Gu and Yong Xu",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 12th Chinese Conference on Biometric Recognition, CCBR 2017 ; Conference date: 28-10-2017 Through 29-10-2017",
year = "2017",
doi = "10.1007/978-3-319-69923-3\_65",
language = "英语",
isbn = "9783319699226",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "605--614",
editor = "Yunhong Wang and Yu Qiao and Jie Zhou and Jianjiang Feng and Zhenan Sun and Zhenhua Guo and Shiguang Shan and Linlin Shen and Shiqi Yu and Yong Xu",
booktitle = "Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings",
address = "德国",
}