基于深度学习的小样本目标检测综述

A Survey of Small Sample Object Detection Based on Deep Learning

针对在小样本情况下目标检测的问题,对当前小样本条件下的目标检测方法进行归纳总结。列举4类小样本学习方法并介绍其优缺点,介绍目前这几类方法的典型算法;进行小样本目标检测实验设计,通过分析各方法的特点得出其可应用方向;对目前的小样本图像目标检测存在的问题进行讨论。结果表明,该分析能为相关领域的研究者提供更多的思路。

Aiming at the problem of target detection under the condition of small samples,the current target detection methods under the condition of small samples are summarized.This paper lists four kinds of small sample learning methods,introduces their advantages and disadvantages,and introduces the typical algorithms of these methods at present;carries out the experimental design of small sample target detection,and obtains the application direction by analyzing the characteristics of each method;discusses the existing problems of current small sample image target detection.The results show that the analysis can provide more ideas for researchers in related fields.