NDLI: Scale-aware limited deformable convolutional neural networks for traffic sign detection and classification

Content Provider	IET Digital Library
Author	Liu, Zhanwen Shen, Chao Fan, Xing Zeng, Gaowen Zhao, Xiangmo
Abstract	Traffic sign detection and classification is a critical component of intelligent transportation systems, which is applied to inform automatic unmanned driving systems and driving assistance systems about conditions and limits of roads. Although computer vision is widely utilised in traffic sign detection, detection and recognising traffic signs globally remains a great challenge due to the variety of sign types, scale-variance and geometric variations. To address these problems, this study proposes a region-based deep convolutional neural network (CNN) framework for traffic sign detection and classification. Specifically, a multi-branch sample pyramid module is proposed, which is based on multi-branch CNNs for multi-scaled feature exaction. A limited deformable convolutional module is then embedded into the CNN layers to learn the distorted information representation for deformation handing. Moreover, a scale-aware multi-task region proposal network module is applied to detect traffic signs with various scales. The whole network is trained in an end-to-end manner. Finally, experiments are conducted on two public detection data sets to demonstrate the effectiveness of the proposed method.
Starting Page	1712
Ending Page	1722
Page Count	11
ISSN	1751956X
Journal	IET Intelligent Transport Systems
e-ISSN	17519578
Volume Number	14
Issue Number	Issue 12, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2020.0217;jsessionid=3g0pujp7g7722.x-iet-live-01
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/14/12
Publisher Date	2020-09-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Unmanned Driving System Computer Vision Computer Vision And Image Processing Technique Deformable Convolutional Neural Network Driver Information System Driving Assistance System Feature Extraction Image Classification Knowledge Engineering Technique Learning (Artificial Intelligence) Neural Computing Technique Neural Nets Object Detection Optical, Image And Video Signal Processing Recognising Traffic Signs Region Based Deep Convolutional Neural Network Framework Road Traffic Scale Aware Multitask Region Proposal Network Module Sign Types Traffic Engineering Computing Traffic Sign Detection
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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

Two-stage traffic sign detection and recognition based on SVM and convolutional neural networks

Traffic sign recognition using weighted multi-convolutional neural network

Efficient convNets for fast traffic sign recognition

Improved detection method for traffic signs in real scenes applied in intelligent and connected vehicles

Real-time detection of distracted driving based on deep learning

Pedestrian detection framework based on magnetic regional regression

Driving posture recognition by convolutional neural networks

Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units

Joint vehicle detection and distance prediction via monocular depth estimation

Scale-aware limited deformable convolutional neural networks for traffic sign detection and classification

Similar Documents

Two-stage traffic sign detection and recognition based on SVM and convolutional neural networks

Traffic sign recognition using weighted multi-convolutional neural network

Efficient convNets for fast traffic sign recognition

Improved detection method for traffic signs in real scenes applied in intelligent and connected vehicles

Real-time detection of distracted driving based on deep learning

Pedestrian detection framework based on magnetic regional regression

Driving posture recognition by convolutional neural networks

Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units

Joint vehicle detection and distance prediction via monocular depth estimation

Scale-aware limited deformable convolutional neural networks for traffic sign detection and classification