计算机科学
人工智能
分类
卷积神经网络
水准点(测量)
目标检测
分割
深度学习
机器学习
背景(考古学)
对象类检测
Viola–Jones对象检测框架
深层神经网络
特征学习
计算机视觉
模式识别(心理学)
对象(语法)
人工神经网络
任务(项目管理)
分类器(UML)
人脸检测
面部识别系统
生物
古生物学
地理
大地测量学
作者
Yang Liu,Peng Sun,Nickolas M. Wergeles,Yi Shang
标识
DOI:10.1016/j.eswa.2021.114602
摘要
In computer vision, significant advances have been made on object detection with the rapid development of deep convolutional neural networks (CNN). This paper provides a comprehensive review of recently developed deep learning methods for small object detection. We summarize challenges and solutions of small object detection, and present major deep learning techniques, including fusing feature maps, adding context information, balancing foreground-background examples, and creating sufficient positive examples. We discuss related techniques developed in four research areas, including generic object detection, face detection, object detection in aerial imagery, and segmentation. In addition, this paper compares the performances of several leading deep learning methods for small object detection, including YOLOv3, Faster R-CNN, and SSD, based on three large benchmark datasets of small objects. Our experimental results show that while the detection accuracy on small objects by these deep learning methods was low, less than 0.4, Faster R-CNN performed the best, while YOLOv3 was a close second.
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