深度学习中的单阶段小目标检测方法综述

Survey of One-Stage Small Object Detection Methods in Deep Learning

随着深度学习的不断发展,目标检测技术逐步从基于传统的手工检测方法向基于深度神经网络的检测方法转变。在众多基于深度学习的目标检测方法中,基于深度学习的单阶段目标检测方法因其网络结构较简单、运行速度较快以及具有更高的检测效率而被广泛运用。但现有的基于深度学习的单阶段目标检测方法由于小目标物体包含的特征信息较少、分辨率较低、背景信息较复杂、细节信息不明显以及定位精度要求较高等原因,导致在检测过程中对小目标物体的检测效果不理想,使得模型检测精度降低。针对目前基于深度学习的单阶段目标检测方法存在的问题,研究了大量基于深度学习的单阶段小目标检测技术。首先从单阶段目标检测方法的AnchorBox、网络结构、交并比函数以及损失函数等几个方面,系统地总结了针对小目标检测的优化方法;其次列举了常用的小目标检测数据集及其应用领域,并给出在各小目标检测数据集上的检测结果图;最后探讨了基于深度学习的单阶段小目标检测方法的未来研究方向。

With the development of deep learning,object detection technology has gradually changed from traditional manual detection methods to deep neural network detection methods.Among many object detection algorithms based on deep learning,the one-stage object detection method based on deep learning is widely used because of its simple network structure,fast running speed and higher detection efficiency.However,the existing one-stage object detection methods based on deep learning do not have ideal detection results for small target objects in the detection process due to the lack of feature information,low resolution,complicated background information,unobvious details and higher positioning accuracy,which reduces the detection accuracy of the model.Aiming at the existing problems of one-stage object detection method based on deep learning,a large amount of one-stage small object detection technologies based on deep learning are studied.Firstly,the optimization methods for small object detection are systematically summarized from the aspects of Anchor Box,network structure,IoU(intersection over union)and loss function in the one-stage object detection methods.Secondly,the commonly used small object detection datasets and their application fields are listed,and the detection graphs on each small object detection dataset are given.Finally,the future research direction of one-stage small object detection methods based on deep learning is investigated.