Small Object Detection Based on Deep Convolutional Neural Networks:A Review

Abstract

Small object detection has long been one of the most challenging problems in computer vision.Since small objects have the characteristics of small coverage area,low resolution,and lack of feature information,their detection effect is not ideal compared to large-sized objects.In recent years,the small object detection algorithm based on deep convolutional neural networks has developed vigorously,and been successfully used in fields such as satellite remote sensing and driverless vehicles.This survey makes a taxnomy,analysis and comparison of existing algorithms.First,the difficulties of small object detection and common detection datasets are introduced.Second,the existing detection algorithms are systematically described from five aspects:backbone network,pyramid structure,anchor design,optimization of object and a bag of species,to provide ideas for further improving the performance of small object detection algorithms.Then,we briefly summarize the existing small object detection algorithms and analyze their performance of the listed algorithm on common dataset.Finally,the application and the future research direction in the field of small object detection has been prospected.

Keywords

• deep learning|small object detection|multi-scale feature fusion|anchor-free|attention mechanism