Summary of Object Detection Based on Convolutional Neural Network

被引：2

作者：

Wang Xuejiao ^{[1
]}

Zhi Min ^{[1
]}

机构：

[1] Inner Mongolia Normal Univ, Coll Comp Sci & Technol, Hohhot, Inner Mongolia, Peoples R China

来源：

ELEVENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2019) | 2020年 / 11373卷

关键词：

Deep learning; object detection; convolutional neural network;

D O I：

10.1117/12.2557219

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Object detection is one of the most basic and central task in computer vision. Its task is to find all the interested objects in the image, and determine the category and location of the objects. Object detection is widely used and has strong practical value and research prospects. Applications include face detection, pedestrian detection and vehicle detection. In recent years, with the development of convolutional neural network, significant breakthroughs have been made in object detection. This paper describes in detail the classification of object detection algorithms based on deep learning. The algorithms are mainly divided into one-stage object algorithm and two-stage object algorithm, and the general data sets and performance indicators of object detection are briefly introduced.

引用

页数：8