CN115223057A - 多模态遥感图像联合学习的目标检测统一模型 - Google Patents
多模态遥感图像联合学习的目标检测统一模型 Download PDFInfo
- Publication number
- CN115223057A CN115223057A CN202210920610.8A CN202210920610A CN115223057A CN 115223057 A CN115223057 A CN 115223057A CN 202210920610 A CN202210920610 A CN 202210920610A CN 115223057 A CN115223057 A CN 115223057A
- Authority
- CN
- China
- Prior art keywords
- target
- target detection
- image
- network
- frame
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/761—Proximity, similarity or dissimilarity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Abstract
Description
Claims (1)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210920610.8A CN115223057B (zh) | 2022-08-02 | 2022-08-02 | 多模态遥感图像联合学习的目标检测统一模型 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210920610.8A CN115223057B (zh) | 2022-08-02 | 2022-08-02 | 多模态遥感图像联合学习的目标检测统一模型 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN115223057A true CN115223057A (zh) | 2022-10-21 |
CN115223057B CN115223057B (zh) | 2023-04-07 |
Family
ID=83615507
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210920610.8A Active CN115223057B (zh) | 2022-08-02 | 2022-08-02 | 多模态遥感图像联合学习的目标检测统一模型 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115223057B (zh) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115880266A (zh) * | 2022-12-27 | 2023-03-31 | 深圳市大数据研究院 | 一种基于深度学习的肠道息肉检测系统和方法 |
CN116012679A (zh) * | 2022-12-19 | 2023-04-25 | 中国科学院空天信息创新研究院 | 一种基于多层级跨模态交互的自监督遥感表示学习方法 |
CN116310656A (zh) * | 2023-05-11 | 2023-06-23 | 福瑞泰克智能系统有限公司 | 训练样本确定方法、装置和计算机设备 |
CN116434024A (zh) * | 2023-04-21 | 2023-07-14 | 大连理工大学 | 目标特征嵌入的红外与可见光图像融合方法 |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113095249A (zh) * | 2021-04-19 | 2021-07-09 | 大连理工大学 | 鲁棒的多模态遥感图像目标检测方法 |
CN113657232A (zh) * | 2021-08-10 | 2021-11-16 | 大连理工大学 | 基于风格内容解耦的跨域遥感图像目标检测方法 |
CN113850176A (zh) * | 2021-09-22 | 2021-12-28 | 北京航空航天大学 | 一种基于多模态遥感图像细粒度弱特征目标涌现检测方法 |
-
2022
- 2022-08-02 CN CN202210920610.8A patent/CN115223057B/zh active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113095249A (zh) * | 2021-04-19 | 2021-07-09 | 大连理工大学 | 鲁棒的多模态遥感图像目标检测方法 |
CN113657232A (zh) * | 2021-08-10 | 2021-11-16 | 大连理工大学 | 基于风格内容解耦的跨域遥感图像目标检测方法 |
CN113850176A (zh) * | 2021-09-22 | 2021-12-28 | 北京航空航天大学 | 一种基于多模态遥感图像细粒度弱特征目标涌现检测方法 |
Non-Patent Citations (3)
Title |
---|
TING CHEN ETC.: "A simple framework for contrastive learning of visual representations", 《ICML"20: PROCEEDINGS OF THE 37TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING》 * |
XIN YUAN ETC.: "Multimodal Contrastive Training for Visual Representation Learning", 《COMPUTER VISION AND PATTERN RECOGNITION (CS.CV)》 * |
张筱晗等: "基于中心点的遥感图像多方向舰船目标检测", 《光子学报》 * |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116012679A (zh) * | 2022-12-19 | 2023-04-25 | 中国科学院空天信息创新研究院 | 一种基于多层级跨模态交互的自监督遥感表示学习方法 |
CN116012679B (zh) * | 2022-12-19 | 2023-06-16 | 中国科学院空天信息创新研究院 | 一种基于多层级跨模态交互的自监督遥感表示学习方法 |
CN115880266A (zh) * | 2022-12-27 | 2023-03-31 | 深圳市大数据研究院 | 一种基于深度学习的肠道息肉检测系统和方法 |
CN115880266B (zh) * | 2022-12-27 | 2023-08-01 | 深圳市大数据研究院 | 一种基于深度学习的肠道息肉检测系统和方法 |
CN116434024A (zh) * | 2023-04-21 | 2023-07-14 | 大连理工大学 | 目标特征嵌入的红外与可见光图像融合方法 |
CN116434024B (zh) * | 2023-04-21 | 2023-09-12 | 大连理工大学 | 目标特征嵌入的红外与可见光图像融合方法 |
CN116310656A (zh) * | 2023-05-11 | 2023-06-23 | 福瑞泰克智能系统有限公司 | 训练样本确定方法、装置和计算机设备 |
CN116310656B (zh) * | 2023-05-11 | 2023-08-15 | 福瑞泰克智能系统有限公司 | 训练样本确定方法、装置和计算机设备 |
Also Published As
Publication number | Publication date |
---|---|
CN115223057B (zh) | 2023-04-07 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN115223057B (zh) | 多模态遥感图像联合学习的目标检测统一模型 | |
CN112257569B (zh) | 一种基于实时视频流的目标检测和识别方法 | |
CN113095249A (zh) | 鲁棒的多模态遥感图像目标检测方法 | |
CN111444865B (zh) | 一种基于逐步求精的多尺度目标检测方法 | |
CN115187786A (zh) | 一种基于旋转的CenterNet2目标检测方法 | |
CN112395958A (zh) | 基于四尺度深浅层特征融合的遥感图像小目标检测方法 | |
WO2024032010A1 (zh) | 一种基于迁移学习策略的少样本目标实时检测方法 | |
CN116580322A (zh) | 一种地面背景下无人机红外小目标检测方法 | |
Fu et al. | A case study of utilizing YOLOT based quantitative detection algorithm for marine benthos | |
CN114677602A (zh) | 一种基于YOLOv5的前视声呐图像目标检测方法和系统 | |
CN113052136A (zh) | 一种基于改进Faster RCNN的行人检测方法 | |
CN112861871A (zh) | 一种基于目标边界定位的红外目标检测方法 | |
CN117152601A (zh) | 一种基于动态感知区域路由的水下目标检测方法及系统 | |
CN116721398A (zh) | 一种基于跨阶段路由注意力模块和残差信息融合模块的Yolov5目标检测方法 | |
CN113780462B (zh) | 基于无人机航拍图像的车辆检测网络建立方法及其应用 | |
CN116110118A (zh) | 一种基于时空特征互补融合的行人重识别和步态识别方法 | |
Da et al. | Remote sensing image ship detection based on improved YOLOv3 | |
CN114332754A (zh) | 基于多度量检测器的Cascade R-CNN行人检测方法 | |
Zhou et al. | Safety helmet wearing detection and recognition based on YOLOv4 | |
Bai et al. | An optimized mask-guided mobile pedestrian detection network with millisecond scale | |
Jiang et al. | Research progress of single-stage small target detection based on deep learning | |
Hu et al. | Aircraft Targets Detection in Remote Sensing Images with Feature Optimization | |
Yu et al. | Pedestrian detection in crowded scenes based on Cascade R-CNN | |
Liu et al. | Text detection based on bidirectional feature fusion and sa attention mechanism | |
Chen et al. | Target Detection for Mine Remote Sensing Using Deep Learning |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
CB03 | Change of inventor or designer information | ||
CB03 | Change of inventor or designer information |
Inventor after: Zhao Wenda Inventor after: Xie Shigeng Inventor after: Zhao Fan Inventor after: Liu Xinghui Inventor after: Huang Youpeng Inventor after: Li Zhili Inventor before: Zhao Wenda Inventor before: Xie Shigeng Inventor before: Wang Haipeng Inventor before: Zhao Fan Inventor before: Liu Xinghui Inventor before: Huang Youpeng Inventor before: Li Zhili |
|
GR01 | Patent grant | ||
GR01 | Patent grant |