计算机科学
人工智能
领域(数学)
目标检测
卷积神经网络
样品(材料)
任务(项目管理)
对象(语法)
深度学习
领域(数学分析)
机器学习
模式识别(心理学)
系统工程
工程类
数学分析
化学
数学
色谱法
纯数学
作者
Sixu Liu,Yanan You,Haozheng Su,Gang Ma,Wei Yang,Fang Liu
出处
期刊:Remote Sensing
[MDPI AG]
日期:2022-09-06
卷期号:14 (18): 4435-4435
被引量:5
摘要
Recent years have witnessed rapid development and remarkable achievements on deep learning object detection in remote sensing (RS) images. The growing improvement of the accuracy is inseparable from the increasingly complex deep convolutional neural network and the huge amount of sample data. However, the under-fitting neural network will damage the detection performance facing the difficulty of sample acquisition. Thus, it evolves into few-shot object detection (FSOD). In this article, we first briefly introduce the object detection task and its algorithms, to better understand the basic detection frameworks followed by FSOD. Then, FSOD design methods in RS images for three important aspects, such as sample, model, and learning strategy, are respectively discussed. In addition, some valuable research results of FSOD in computer vision field are also included. We advocate a wide research technique route, and some advice about feature enhancement and multi-modal fusion, semantics extraction and cross-domain mapping, fine-tune and meta-learning strategies, and so on, are provided. Based on our stated research route, a novel few-shot detector that focuses on contextual information is proposed. At the end of the paper, we summarize accuracy performance on experimental datasets to illustrate the achievements and shortcomings of the stated algorithms, and highlight the future opportunities and challenges of FSOD in RS image interpretation, in the hope of providing insights into future research.
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