计算机工程与应用2019，Vol.55Issue(11)：25-34,10.DOI:10.3778/j.issn.1002-8331.1902-0254
基于深度学习的目标检测框架进展研究
Research on Progress of Object Detection Framework Based on Deep Learning
寇大磊 1权冀川 2张仲伟1
作者信息
- 1. 陆军工程大学 指挥控制工程学院,南京 210007
- 2. 中国人民解放军68023部队
- 折叠
摘要
Abstract
After the R-CNN framework is proposed, the object detection framework based on deep learning has gradually become the mainstream, which can be divided into one-stage and two-stage. In the past two years, based on the classic deep learning object detection frameworks such as Faster R-CNN, YOLO, and SSD, a large number of excellent frameworks have emerged. Firstly, according to the optimization method, the frameworks proposed in the past few years are sorted out and summarized. Then, the performance of the object detection methods is compared on the mainstream test sets such as PASCAL_VOC and MS COCO. The advantages and disadvantages are analyzed. Finally, the current difficulties and challenges in the field are discussed, and the possible development directions are prospected.关键词
深度学习/目标检测/卷积神经网络/计算机视觉Key words
deep learning/object detection/convolutional neural networks/computer vision分类
信息技术与安全科学引用本文复制引用
寇大磊,权冀川,张仲伟..基于深度学习的目标检测框架进展研究[J].计算机工程与应用,2019,55(11):25-34,10.
