@inproceedings{fb6c473c67bd4a9cb9671b1566a18ee4,
title = "Object detection based on DenseNet and RPN",
abstract = "Object detection algorithm based on depth model has achieved state-of-the-art results on various challenging benchmarks. However, the large amount of parameters of depth model means a large amount of calculation. This seriously limits the practical application of object detection algorithm, especially on embedded devices with limited computing power. We propose an object detection algorithm based on DenseNet and Region Proposal Network(RPN) and replace ROI Pooling with ROI Align. From the evaluation on PASCAL VOC and MS COCO we can see that the algorithm achieves object detection with fewer parameters while maintaining or improving accuracy. This is meaningful for the development of embedded in-depth learning. Finally, we explore the influence of different shared convolutional layers on object detection algorithm.",
keywords = "Feature Extraction, Object Detection, Parameter Efficiency, Shared Convolution",
author = "Jing Li and Wenjie Chen and Yangyang Sun and Ye Li and Zhihong Peng",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8866610",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8410--8415",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "United States",
}