基于深度学习的小目标检测研究与应用综述
A Survey of Research and Application of Small Object Detection Based on Deep Learning
查看参考文献56篇
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文摘
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目标检测在基于传统手工特征及深度学习算法上已经取得较大发展,然而针对小目标检测的研究近几年才开始出现,研究成果较少,且大都是在已有目标检测算法基础上进行改进,以提高小目标检测的检测精度.小目标像素点少,本身携带的特征少,多次下采样后就更难进行特征提取,因而小目标检测面临极大挑战.小目标检测在自动驾驶、遥感图像检测、刑侦等领域都有广泛应用需求,对于小目标检测技术的研究有重要的实用价值.本文对小目标检测的现有研究成果进行了详细综述.首先,将现有算法按照检测需要的阶段数分为一阶段、二阶段、多阶段,描述了RetinaNet、CornerNet-Lite、特征金字塔网络(Feature Pyramid Network,FPN)等算法的原理并进行了对比分析.其次,本文描述了小目标检测技术在不同领域的应用情况,并汇总了MS COCO、PASCAL VOC、DOTA、KITTI等数据集及算法性能评价指标.最后,总结了小目标检测面临的挑战,并展望了未来的研究方向. |
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其他语种文摘
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Object detection has been well studied based on the traditional manual features and deep learning algorithm.However,the research on small object detection has just begun in recent years and there are little research outcome available.Furthermore,most of the methods proposed are based on the traditional object detection algorithms with certain modifications so as to improve the accuracy of small object detection.Small object contains fewer pixels and has less features,and it is even harder to extract object features after down sampling.Hence,small object detection is a challenging task.Small object detection has a wide range of application requirements in the fields of automatic driving,remote sensing image detection,and criminal investigation.It has important practical value for the research of small object detection technology.In this paper,the existing research results of small object detection are summarized.Firstly,the existing algorithms are classified into one stage,two stages and multi-stages according to the number of stages for detection.The principles of RetinaNet、CornerNet-Lite、feature pyramid network(FPN)and other algorithms are described and compared.Secondly,this paper describes the application of small object detection technology in different fields,and summarizes the data sets such as MS COCO、PASCAL VOC、DOTA、KITTI and algorithm performance evaluation indicators.Finally,the challenges faced by small object detection are concluded,and the future research directions are prospected. |
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来源
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电子学报
,2020,48(3):590-601 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2020.03.024
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关键词
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小目标检测
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尺度变换
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特征金字塔
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深度学习
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特征提取
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卷积神经网络
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地址
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1.
电子信息现场勘验应用技术公安部重点实验室, 电子信息现场勘验应用技术公安部重点实验室, 陕西, 西安, 710121
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陕西省无线通信与信息处理技术国际合作研究中心, 陕西省无线通信与信息处理技术国际合作研究中心, 陕西, 西安, 710121
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西安邮电大学图像与信息处理研究所, 陕西, 西安, 710121
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语种
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中文 |
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文献类型
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综述型 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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陕西省国际合作交流项目
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国家自然科学基金
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国家重点研发计划
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文献收藏号
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CSCD:6770107
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