Deep learning

Machine learning

Geodesy

Paleontology

Pattern recognition (psychology)

Artificial intelligence

Benchmark (surveying)

Geography

Object detection

Context (archaeology)

Object (grammar)

Deep neural networks

Facial recognition system

Computer vision

Computer science

Face detection

Feature learning

Object-class detection

Convolutional neural network

Biology

Segmentation

Expert Systems with Applications

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.

A survey and performance evaluation of deep learning methods for small object detection