SLMS-SSD: Improving the balance of semantic and spatial information in object detection
文献类型：期刊论文
| 作者 | Wang, Kunfeng3 ; Wang, Yadong3; Zhang, Shuqin3; Tian, Yonglin1,2; Li, Dazi3
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| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 出版日期 | 2022-11-15 |
| 卷号 | 206页码:10 |
| 关键词 | Object detection Deep learning Multi-scale feature selection Self-learning feature fusion |
| ISSN号 | 0957-4174 |
| DOI | 10.1016/j.eswa.2022.117682 |
| 通讯作者 | Wang, Kunfeng(wangkf@buct.edu.cn) |
| 英文摘要 | With the development of deep learning technology, the research on convolutional neural network-based object detection is becoming more and more mature. However, most methods are unsatisfactory in dealing with the issue of semantic and spatial information imbalance. In this article, we extend the single-shot multibox detector SSD and propose a self-learning multi-scale object detection network by balancing the semantic information and spatial information, named SLMS-SSD. We first construct a shallow feature enhancement module to enhance the representation of small objects by extracting richer context information. Second, in terms of feature connectivity, we design a multi-scale feature selection module for intermediate layer features with a combination of top-down and direct up-samplings. Finally, in terms of feature strength, we design a self-learning feature fusion module for measuring the feature importance. We validate our model on the PASCAL VOC and MS COCO datasets, and the results demonstrate that it can effectively improve the accuracy of object detection, especially the accuracy of small object detection. |
| 资助项目 | National Key R&D Pro-gram of China[2020YFC2003900] ; National Natural Sci-ence Foundation of China[62076020] ; Fundamen-tal Research Funds for the Central Universities, China[buctrc201933] |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000832966000001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Key R&D Pro-gram of China ; National Natural Sci-ence Foundation of China ; Fundamen-tal Research Funds for the Central Universities, China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49848]  |
| 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.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China 3.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Kunfeng,Wang, Yadong,Zhang, Shuqin,et al. SLMS-SSD: Improving the balance of semantic and spatial information in object detection[J]. EXPERT SYSTEMS WITH APPLICATIONS,2022,206:10. |
| APA | Wang, Kunfeng,Wang, Yadong,Zhang, Shuqin,Tian, Yonglin,&Li, Dazi.(2022).SLMS-SSD: Improving the balance of semantic and spatial information in object detection.EXPERT SYSTEMS WITH APPLICATIONS,206,10. |
| MLA | Wang, Kunfeng,et al."SLMS-SSD: Improving the balance of semantic and spatial information in object detection".EXPERT SYSTEMS WITH APPLICATIONS 206(2022):10. |
入库方式： OAI收割
来源：自动化研究所
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