Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY |
ISSN | 0018-9545 |
2018-09-01 | |
卷号 | 67期号:9页码:8019-8030 |
文章类型 | Article |
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. |
关键词 | Traffic Object Detection Convolutional Neural Network Multi-scale Features Global Information |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TVT.2018.2843394 |
关键词[WOS] | INTELLIGENT TRANSPORTATION SYSTEMS ; VEHICLE DETECTION ; RECOGNITION ; VISION ; CLASSIFICATION ; MANAGEMENT ; NETWORKS ; TRACKING |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61533019 ; 91720000) |
WOS研究方向 | Engineering ; Telecommunications ; Transportation |
WOS类目 | Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology |
WOS记录号 | WOS:000445397600010 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/27905 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Wang, Kunfeng |
作者单位 | 1.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 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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