期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 172, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2021.114602
关键词
Small object detection; Computer vision; Convolutional neural networks; Deep learning
This paper reviews deep learning methods for small object detection, discussing challenges, solutions, and techniques. Experimental results show that Faster R-CNN performs the best in detecting small objects.
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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