A real-time object detection algorithm for video

Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, w...

Full description

Saved in:
Bibliographic Details
Published in:Computers & electrical engineering Vol. 77; pp. 398 - 408
Main Authors: Lu, Shengyu, Wang, Beizhan, Wang, Hongji, Chen, Lihao, Linjian, Ma, Zhang, Xiaoyan
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Ltd 01.07.2019
Elsevier BV
Subjects:
Algorithms
Cameras
Computing time
Convolution
Deep learning
GoogleNet
Image detection
Machine learning
Object detection
Object recognition
Real time
Video
Vision systems
YOLO
YOLO
GoogleNet
Real-time
Video
Object detection
ISSN:0045-7906, 1879-0755
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, we propose a real-time object detection algorithm for videos based on the YOLO network. We eliminate the influence of the image background by image preprocessing, and then we train the Fast YOLO model for object detection to obtain the object information. Based on the Google Inception Net (GoogLeNet) architecture, we improve the YOLO network by using a small convolution operation to replace the original convolution operation, which can reduce the number of parameters and greatly shorten the time for object detection. Our Fast YOLO algorithm can be applied to real-time object detection in video.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2019.05.009