CASIA OpenIR
MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection
Zhang, Hui1,2; Wang, Kunfeng1,3; Tian, Yonglin1; Gou, Chao1; Wang, Fei-Yue1,4
Source PublicationIEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
ISSN0018-9545
2018-09-01
Volume67Issue:9Pages:8019-8030
SubtypeArticle
AbstractObject detection plays an important role in intelligent transportation systems and intelligent vehicles. Although the topic of object detection has been studied for decades, it is still challenging to accurately detect objects under complex scenarios. The contributing factors for challenges include diversified object and background appearance, motion blur, adverse weather conditions, and complex interactions among objects. In this paper, we propose a new convolutional neural network (CNN) model for traffic object detection, by using multi-scale local and global feature representation (MFR). The proposed model consists of two components: a region proposal network that generates candidate object regions and an object detection network that incorporates multi-scale features and global information, namely MFR-CNN. These two components are jointly optimized. Once the system is trained, it can detect real-world traffic objects accurately, especially small objects and heavily occluded objects. We evaluate the proposed method on four benchmark datasets, achieving consistent improvements over the state of the art.
KeywordTraffic Object Detection Convolutional Neural Network Multi-scale Features Global Information
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TVT.2018.2843394
WOS KeywordINTELLIGENT TRANSPORTATION SYSTEMS ; VEHICLE DETECTION ; RECOGNITION ; VISION ; CLASSIFICATION ; MANAGEMENT ; NETWORKS ; TRACKING
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61533019 ; 91720000)
WOS Research AreaEngineering ; Telecommunications ; Transportation
WOS SubjectEngineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS IDWOS:000445397600010
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27905
Collection中国科学院自动化研究所
Corresponding AuthorWang, Kunfeng
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Qingdao Acad Intelligent Ind, Innovat Ctr Parallel Vis, Qingdao 266000, Peoples R China
4.Natl Univ Def Technol, Res Ctr Computat Experiments & Parallel Syst Tech, Changsha 410073, Hunan, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Hui,Wang, Kunfeng,Tian, Yonglin,et al. MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2018,67(9):8019-8030.
APA Zhang, Hui,Wang, Kunfeng,Tian, Yonglin,Gou, Chao,&Wang, Fei-Yue.(2018).MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,67(9),8019-8030.
MLA Zhang, Hui,et al."MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 67.9(2018):8019-8030.
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