NDLI: Accurate and fast single shot multibox detector

Content Provider	IET Digital Library
Author	Guo, Lie Wang, Dongxing Li, Linhui Feng, Jindun
Abstract	With the development of deep learning, the performance of object detection has made great progress. However, there are still some challenging problems, such as the detection accuracy of small objects and the efficiency of the detector. This study proposes an accurate and fast single shot multibox detector, which includes context comprehensive enhancement (CCE) module and feature enhancement module (FEM). To integrate more efficient information when aggregating context information, the conv4_3 and fc_7 feature maps are merged to design the CCE module. To obtain more fine-grained feature information, this study presents a FEM and special feature enhancement module (FEM-s) module that can fuse different receptive field sizes to better adapt to the scale change of the object. Compared to existing methods based on deep learning, the proposed method helps to gradually produce more detailed feature maps with better performance. Under the premise of ensuring real-time speed, the authors network can achieve 81.2 mean average precision on the PASCAL VOC 2007 test with an input size of 320 × 320 on a single Nvidia 2080Ti GPU.
Starting Page	391
Ending Page	398
Page Count	8
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 6, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0711
Journal	IET Computer Vision
Publisher Date	2020-04-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CCE Module Computer Vision And Image Processing Technique Context Information Conv4_3 Feature Maps Convolutional Neural Nets Deep Learning Fast Single Shot Multibox Detector Fc_7 Feature Maps Feature Enhancement Module Feature Extraction FEM-s Module Fine-grained Feature Information Image Representation Image Segmentation Learning in AI Neural Computing Technique Nvidia 2080Ti GPU Object Detection Optical, Image And Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Scale specified single shot multibox detector

Fast single shot multibox detector and its application on vehicle counting system

Object detection method on station logo with single shot multi-box detector

ASSD: Attentive single shot multibox detector

Small vehicles detection based on UAV

A New Automated Polyp Detection Network MP-FSSD in WCE and Colonoscopy Images Based Fusion Single Shot Multibox Detector and Transfer Learning

Application of convolution neural network object detection algorithm in logistics warehouse

YOLOpeds: efficient real-time single-shot pedestrian detection for smart camera applications

Regional attention-based single shot detector for SAR ship detection

Accurate and fast single shot multibox detector

Similar Documents

Scale specified single shot multibox detector

Fast single shot multibox detector and its application on vehicle counting system

Object detection method on station logo with single shot multi-box detector

ASSD: Attentive single shot multibox detector

Small vehicles detection based on UAV

A New Automated Polyp Detection Network MP-FSSD in WCE and Colonoscopy Images Based Fusion Single Shot Multibox Detector and Transfer Learning

Application of convolution neural network object detection algorithm in logistics warehouse

YOLOpeds: efficient real-time single-shot pedestrian detection for smart camera applications

Regional attention-based single shot detector for SAR ship detection

Accurate and fast single shot multibox detector