Cross-Connected Bidirectional Pyramid Network for Infrared Small-Dim Target Detection | |
Bai, Yuanning5; Li, Ruimin4; Gou, Shuiping5; Zhang, Chenchen3; Chen, Yaohong2; Zheng, Zhihui1 | |
刊名 | IEEE Geoscience and Remote Sensing Letters |
2022 | |
卷号 | 19 |
关键词 | Infrared small-dim target detection crossconnected bidirectional pyramid network ROI feature augment regular constraint loss |
ISSN号 | 1545598X; 15580571 |
DOI | 10.1109/LGRS.2022.3145577 |
产权排序 | 4 |
英文摘要 | Infrared small-dim target detection is an important technology in the fields of infrared guidance, anti-missile, and tracking system. Due to the small size of targets, no obvious structure information, and low image signal-to-noise ratio, infrared small-dim target detection is still a challenging task. In this letter, a cross-connected bidirectional pyramid network (CBP-Net) is proposed for infrared small-dim target detection. The main body of the CBP-Net is to embed a bottom-up pyramid in the Feature Pyramid Network (FPN), which is designed to provide more comprehensive target information by connecting with the original multi-scale features and the top-down pyramid. The bottom-up pyramid together with the top-down pyramid forms the proposed bidirectional pyramid structure. Then, an ROI feature augment module composed of deformable ROI pooling and position attention is designed to fuse multi-scale ROI features and enhance the spatial information of the small-dim target. Besides, a regular constraint loss is introduced to restrict multi-scale feature fusion to learn more precise target location information. Experimental results on two challenging datasets show that the performance of the proposed CBP-Net is superior to the state-of-the-art methods. IEEE |
语种 | 英语 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
WOS记录号 | WOS:000757847800002 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/95694] |
专题 | 西安光学精密机械研究所_动态光学成像研究室 |
作者单位 | 1.Beijing Aerospace Automatic Control Institute, Beijing 100070, China. 2.Xi'an institute of optics and precision mechanics, CAS, Xi'an 710119, China.; 3.Dalian Maritime University, Dalian 116026, China.; 4.Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an 710071, China. (e-mail: rmli@xidian.edu.cn); 5.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an 710071, China.; |
推荐引用方式 GB/T 7714 | Bai, Yuanning,Li, Ruimin,Gou, Shuiping,et al. Cross-Connected Bidirectional Pyramid Network for Infrared Small-Dim Target Detection[J]. IEEE Geoscience and Remote Sensing Letters,2022,19. |
APA | Bai, Yuanning,Li, Ruimin,Gou, Shuiping,Zhang, Chenchen,Chen, Yaohong,&Zheng, Zhihui.(2022).Cross-Connected Bidirectional Pyramid Network for Infrared Small-Dim Target Detection.IEEE Geoscience and Remote Sensing Letters,19. |
MLA | Bai, Yuanning,et al."Cross-Connected Bidirectional Pyramid Network for Infrared Small-Dim Target Detection".IEEE Geoscience and Remote Sensing Letters 19(2022). |
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