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MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection

文献类型：期刊论文

作者Zhang, Hui2,3; Wang, Kunfeng1,2; Tian, Yonglin2; Gou, Chao2; Wang, Fei-Yue2,4
刊名IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
出版日期2018-09-01
卷号67期号:9页码:8019-8030
关键词Traffic Object Detection Convolutional Neural Network Multi-scale Features Global Information
ISSN号0018-9545
DOI10.1109/TVT.2018.2843394
文献子类Article
英文摘要Object 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.
WOS关键词INTELLIGENT TRANSPORTATION SYSTEMS ; VEHICLE DETECTION ; RECOGNITION ; VISION ; CLASSIFICATION ; MANAGEMENT ; NETWORKS ; TRACKING
WOS研究方向Engineering ; Telecommunications ; Transportation
语种英语
WOS记录号WOS:000445397600010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China(61533019 ; 91720000)
源URL[http://ir.ia.ac.cn/handle/173211/27904]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Wang, Kunfeng
作者单位1.Qingdao Acad Intelligent Ind, Innovat Ctr Parallel Vis, Qingdao 266000, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Natl Univ Def Technol, Res Ctr Computat Experiments & Parallel Syst Tech, Changsha 410073, Hunan, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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