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A Survey and Performance Evaluation of Deep Learning Methods for Small Object Detection - 2021

A Survey And Performance Evaluation Of Deep Learning Methods For Small Object Detection

Survey Paper on Performance Evaluation Of Deep Learning Methods For Small Object Detection

Research Area:  Machine Learning


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.

Deep Learning Methods
Small Object Detection
deep convolutional neural networks
computer vision

Author(s) Name:  Yang Liu, Peng Sun, Nickolas Wergeles, Yi Shang

Journal name:  Expert Systems with Applications

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.eswa.2021.114602

Volume Information:  Volume 172, 15 June 2021, 114602