基于深度学习的小目标检测基准研究进展

童康; 吴一全

doi:10.12263/DZXB.20230624

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基于深度学习的小目标检测基准研究进展

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- 基于深度学习的小目标检测基准研究进展
- Research Advances on Deep Learning Based Small Object Detection Benchmarks
- 电子学报 2024年52卷第3期页码：1016-1040
- 作者机构：
  
  南京航空航天大学电子信息工程学院，江苏南京 211106
- 作者简介：
  
  [ "童康男，1992年2月出生于江苏省南京市.现为南京航空航天大学电子信息工程学院博士研究生.主要研究方向为计算机视觉、模式识别、小目标检测. E-mail: tkangcv@nuaa.edu.cn" ]
  [ "吴一全男，1963年1月出生于江苏省启东市.现为南京航空航天大学电子信息工程学院教授、博士生导师.主要研究方向为遥感图像处理与理解、红外小目标检测与识别、视觉检测与图像测量、视频处理与智能分析等. E-mail: nuaaimage@163.com" ]
- 基金信息：
  
  国家自然科学基金(61573183)
- DOI：10.12263/DZXB.20230624
  中图分类号： TP389.1;TP391.41
- 收稿：2023-07-03，
  
  修回：2023-12-20，
  
  纸质出版：2024-03-25
- 稿件说明：
移动端阅览
童康, 吴一全. 基于深度学习的小目标检测基准研究进展[J]. 电子学报, 2024, 52(03): 1016-1040.

TONG Kang, WU Yi-quan. Research Advances on Deep Learning Based Small Object Detection Benchmarks[J]. Acta Electronica Sinica, 2024, 52(03): 1016-1040.
童康, 吴一全. 基于深度学习的小目标检测基准研究进展[J]. 电子学报, 2024, 52(03): 1016-1040. DOI：10.12263/DZXB.20230624

TONG Kang, WU Yi-quan. Research Advances on Deep Learning Based Small Object Detection Benchmarks[J]. Acta Electronica Sinica, 2024, 52(03): 1016-1040. DOI：10.12263/DZXB.20230624

摘要

小目标检测是计算机视觉中极具挑战性的任务.它被广泛应用于遥感、交通、国防军事和日常生活等领域.相比其他视觉任务，小目标检测的研究进展相对缓慢.制约因素除了学习小目标特征的内在困难，还有小目标检测基准，即小目标检测数据集的稀缺以及建立小目标检测评估指标的挑战.为了更深入地理解小目标检测，本文首次对基于深度学习的小目标检测基准进行了全新彻底的调查.系统介绍了现存的35个小目标数据集，并从相对尺度和绝对尺度（目标边界框的宽度或高度、目标边界框宽高的乘积、目标边界框面积的平方根）对小目标的定义进行全面总结.重点从基于交并比及其变体、基于平均精度及其变体以及其他评估指标这3方面详细探讨了小目标检测评估指标.此外，从锚框机制、尺度感知与融合、上下文信息、超分辨率技术以及其他改进思路这5个角度对代表性小目标检测算法进行了全面阐述.与此同时，在6个数据集上对典型评估指标（评估指标+目标定义、评估指标+单目标类别）下的代表性小目标检测算法进行性能的深入分析与比较，并从小目标检测新基准、小目标定义的统一、小目标检测新框架、多模态小目标检测算法、旋转小目标检测以及高精度且实时的小目标检测这6个方面指出未来可能的发展趋势.希望该综述可以启发相关研究人员，进一步促进小目标检测的发展.

Abstract

Small object detection is an extremely challenging task in computer vision. It is widely used in remote sensing

intelligent transportation

national defense and military

daily life and other fields. Compared to other visual tasks such as image segmentation

action recognition

object tracking

generic object detection

image classification

video caption and human pose estimation

the research progress of small object detection is relatively slow. We believe that the constraints mainly include two aspects: the intrinsic difficulty of learning small object features and the scarcity of small object detection benchmarks. In particular

the scarcity of small object detection benchmarks can be considered from two aspects: the scarcity of small object detection datasets and the difficulty of establishing evaluation metrics for small object detection. To gain a deeper understanding of small object detection

this article conducts a brand-new and thorough investigation on small object detection benchmarks based on deep learning for the first time. The existing 35 small object detection datasets are introduced from 7 different application scenarios

such as remote sensing images

traffic sign and traffic light detection

pedestrian detection

face detection

synthetic aperture radar images and infrared images

daily life and others. Meanwhile

comprehensively summarize the definition of small objects from both relative scale and absolute scale. For the absolute scale

it mainly includes 3 categories: the width or height of the object bounding box

the product of the width and height of the object bounding box

and the square root of the area of the object bounding box. The focus is on exploring the evaluation metrics of small object detection in detail from 3 aspects: based on IoU (Intersection over Union) and its variants

based on average precision and its variants

and other evaluation metrics. In addition

in-depth analysis and comparison of the performance of some representative small object detection algorithms under typical evaluation metrics are conducted on 6 datasets. These categories of typical evaluation metrics can be further subdivided

including the evaluation metric plus the definition of objects

the evaluation metric plus single object category. More concretely

the evaluation metrics plus the definition of objects can be divided into 4 categories: average precision plus the definition of objects

miss rate plus the definition of objects

DoR-AP-SM (Degree of Reduction in Average Precision between Small objects and Medium objects) and DoR-AP-SL (Degree of Reduction in Average Precision between Small objects and Large objects). For the evaluation metrics plus single object category

it mainly includes 2 types: average precision plus single object category

OLRP (Optimal Localization Recall Precision) plus single object category. These representative small object detection methods mainly include anchor mechanism

scale-aware and fusion

context information

super-resolution technique and other improvement ideas. Finally

we point out the possible trends in the future from 6 aspects: a new benchmark for small object detection

a unified definition of small objects

a new framework for small object detection

multi-modal small object detection algorithms

rotating small object detection

and high precision and real time small object detection. We hope that this paper could provide a timely and comprehensive review of the research progress of small object detection benchmarks based on deep learning

and inspire relevant researchers to further promote the development of this field.

关键词

Keywords

references

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