Review of Target Detection Algorithm Based on Deep Learning
作者单位：School of Information Science and Technology of South China Business College Guangdong University of Foreign Studies
会议日期：2020年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Target detection Deep learning Computer vision
摘 要：In recent years, artificial intelligence（AI） technology has developed rapidly, and personal safety, social safety, and national security have attracted more and more attention. Deep learning is widely used in different kinds of fields, among which target detection has made continuous breakthroughs in image detection or video processing. Target detection should be real-time and accurate, which is the requirement of people for the effect of target detection, while traditional target detection has been difficult to meet its requirements. Target detection algorithm based on deep learning has become the mainstream in this field. This paper mainly introduced two-stage models based on region detection classification: R-CNN, SPP-NET, Fast R-CNN, Faster R-CNN, and the advantages and disadvantages of the target detection algorithm YOLO and SSD based on regression single-stage model, and summarized and prospected the development direction of target detection.