基于深度学习的目标检测算法研究综述

A Survey of Research on Target Detection Algorithms Based on Deep Learning

传统的目标检测算法主要依赖于人工选取的特征来对物体进行检测。人工提取的特征对主要针对某些特定对象,比如有的特征适合做边缘检测,有的适合做纹理检测,不具有普遍性。近年来,深度学习蓬勃发展,在计算机视觉领域比如图像分类、目标检测、图像语义分割等方面取得了重大的进展。深度学习作为一种特征学习方法能够自动学习到目标的有用特征,避免了人工提取特征,同时能够保证良好的检测效果。本文首先介绍基于深度学习的目标检测算法研究进展,其次总结目标检测算法中常见的难题与解决措施,最后对目标检测算法的可能发展方向进行展望。

Traditional target detection algorithms rely mainly on manually selecting features to detect objects.The artificially extracted feature pairs are mainly for certain specific objects,such as some features suitable for edge detection,and some suitable for texture detection,which is not universal.In recent years,deep learning has flourished,and significant research progress has been made in the field of computer vision such as image classification,target detection,and image semantic segmentation.As a feature learning method,deep learning can automatically learn the useful features of the target,avoiding the problem of manual extraction of features,and at the same time ensuring good detection results.Firstly,the research progress of target detection algorithm based on deep learning is introduced.Secondly,the common problems and solutions in target detection algorithm are summarized.Finally,the possible development direction of target detection algorithm is prospected.