### Research Progress of Object Detection Based on Weakly Supervised Learning

YANG Hui, QUAN Jichuan, LIANG Xinyu, WANG Zhongwei

1. 1.Command & Control Engineering College, Army Engineering University of PLA, Nanjing 210007, China
2.Unit 73658 of PLA, China
• Online:2021-08-15 Published:2021-08-16

### 基于弱监督学习的目标检测研究进展

1. 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73658部队

Abstract:

With the continuous development of Convolutional Neural Network（CNN）, as the most basic technology in computer vision, object detection has made remarkable progress. Firstly, the current situation that the strong supervised object detection algorithm requires high precision for labeling datasets is introduced. Secondly, the object detection algorithm based on weakly supervised learning is studied. The algorithm is classified into four categories according to different feature processing methods, and the advantages and disadvantages of each algorithm are analyzed and compared. Thirdly, the detection accuracy of all kinds of object detection algorithms based on weakly supervised learning is compared through experiments. At the same time, it is compared with the mainstream strong supervised object detection algorithms. Finally, the future research hotspots of object detection algorithms based on weakly supervised learning are prospected.