Abstract

As a classic subject in the field of image processing and computer vision, target detection has a wide range of applications in traffic monitoring, image retrieval, human-computer interaction and so on. It aims at detecting objects of interest in a static image. In view of the strong expressive ability of convolutional neural networks in deep learning, this paper presents the classical detection framework R-CNN of deep learning. Based on the above detection framework, the functional requirements, such as data pre-processing, training model and image prediction, as well as the non-functional requirements of the target detection system are analysed. According to the above requirements, a target detection system based on deep learning is developed. Practice has proved that the system has good performance in terms of hardware and performance.

Details

Title
Research on Target Detection Methods under the Concept of Deep Learning
Author
Xu, Hang 1 ; Huang, Zhigong 1 

 College of Electronic Engineering, Guangxi Normal University, Guilin, China 
Publication year
2018
Publication date
Sep 2018
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2572552845
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.