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公开(公告)号：US20220230324A1
公开(公告)日：2022-07-21
申请号：US17336748
申请日：2021-06-02
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin YANG , Haiyang MEI , Wen DONG , Xiaopeng WEI , Dengping FAN
Abstract: A method for segmenting a camouflaged object image based on distraction mining is disclosed. PFNet successively includes a multi-layer feature extractor, a positioning module, and a focusing module. The multi-layer feature extractor uses a traditional feature extraction network to obtain different levels of contextual features; the positioning module first uses RGB feature information to initially determine the position of the camouflaged object in the image; the focusing module mines the information and removes the distraction information based on the image RGB feature information and preliminary position information, and finally determines the boundary of the camouflaged object step by step. The method of the present invention introduces the concept of distraction information into the problem of segmentation of the camouflaged object and develops a new information exploration and distraction information removal strategy to help the segmentation of the camouflaged object image.
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公开(公告)号：US20220212339A1
公开(公告)日：2022-07-07
申请号：US17564588
申请日：2021-12-29
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin YANG , Boyan WEI , Baocai YIN , Qiang ZHANG , Xiaopeng WEI , Zhenjun DU
IPC: B25J9/16
Abstract: The present invention belongs to the technical field of computer vision and provides a data active selection method for robot grasping. The core content of the present invention is a data selection strategy module, which shares the feature extraction layer of backbone main network and integrates the features of three receptive fields with different sizes. While making full use of the feature extraction module, the present invention greatly reduces the amount of parameters that need to be added. During the training process of the main grasp method detection network model, the data selection strategy module can be synchronously trained to form an end-to-end model. The present invention makes use of naturally existing labeled and unlabeled labels, and makes full use of the labeled data and the unlabeled data. When the amount of the labeled data is small, the network can still be more fully trained.
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公开(公告)号：US20240257304A1
公开(公告)日：2024-08-01
申请号：US18004800
申请日：2022-06-15
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Qian LIU , Bing WU , Qiang ZHANG , Xiaopeng WEI
IPC: G06T3/4053 , B25J9/16 , G06T3/4046
CPC classification number: G06T3/4053 , B25J9/163 , B25J9/1694 , G06T3/4046
Abstract: The present disclosure relates to a tactile pattern Super Resolution (SR) reconstruction method and acquisition system, which belong to the field of tactile perception. First, a High Resolution (HR) tactile pattern sample is obtained by using a Low Resolution (LR) tactile sensor; then, a deep learning-based tactile SR model is trained by using a tactile SR data set; and finally, reconstructing the tactile data of a contact surface to be measured as an SR tactile pattern by using the tactile SR model. The present disclosure uses the existing taxel-based LR tactile sensor and adopts a deep learning-based tactile SR reconstruction technology, which can effectively restore the shape of the contact surface, improves the resolution of the tactile sensor, and meanwhile, maintains the characteristics of the sensor being light, flexible, and easy to be integrated into devices, such as a robot.
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公开(公告)号：US20220215662A1
公开(公告)日：2022-07-07
申请号：US17557933
申请日：2021-12-21
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin YANG , Xiaopeng WEI , Yu QIAO , Qiang ZHANG , Baocai YIN , Haiyin PIAO , Zhenjun DU
IPC: G06V20/40 , G06T7/10 , G06V10/46 , G06V10/82 , G06T3/40 , G06T7/215 , G06T9/00 , G06K9/62 , G06V10/72 , G06V10/764 , G06V10/778 , G06V10/774 , G06V10/776
Abstract: The present invention belongs to the technical field of computer vision, and provides a video semantic segmentation method based on active learning, comprising an image semantic segmentation module, a data selection module based on the active learning and a label propagation module. The image semantic segmentation module is responsible for segmenting image results and extracting high-level features required by the data selection module; the data selection module selects a data subset with rich information at an image level, and selects pixel blocks to be labeled at a pixel level; and the label propagation module realizes migration from image to video tasks and completes the segmentation result of a video quickly to obtain weakly-supervised data. The present invention can rapidly generate weakly-supervised data sets, reduce the cost of manufacture of the data and optimize the performance of a semantic segmentation network.
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公开(公告)号：US20230169309A1
公开(公告)日：2023-06-01
申请号：US17992775
申请日：2022-11-22
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin YANG , Xiaopeng WEI , Li ZHU , Xirong XU , Chenming DUAN
IPC: G06N3/042 , G06N3/0442
CPC classification number: G06N3/042 , G06N3/0442
Abstract: The present invention belongs to the technical field of knowledge graph, and provides a knowledge graph construction method for an ethylene oxide derivatives production process. According to data types and characteristics, data sources of the ethylene oxide derivatives production process are sorted and divided into three types: structural data, unstructured data and other types of data. An ontology layer and a data layer of a knowledge graph are constructed by combining top-down and bottom-up methods. A data-driven incremental ontology modeling method is proposed to ensure the expandability of the knowledge graph. For structural knowledge extraction, the safety of original data storage is ensured by means of virtual knowledge graph, and a new mapping mechanism is proposed to realize data materialization. For unstructured knowledge extraction, an entity extraction task is realized on the basis of a BERT-BiLSTM-CRF named entity recognition model by integrating a pre-training language model BERT.
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Patent Agency Ranking
公开(公告)号：US20220230322A1
公开(公告)日：2022-07-21
申请号：US17336702
申请日：2021-06-02
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Wen DONG , Xin YANG , Haiyang MEI , Xiaopeng WEI , Qiang ZHANG
Abstract: The invention belongs to scene segmentation's field in computer vision and is a depth-aware method for mirror segmentation. PDNet successively includes a multi-layer feature extractor, a positioning module, and a delineating module. The multi-layer feature extractor uses a traditional feature extraction network to obtain contextual features; the positioning module combines RGB feature information with depth feature information to initially determine the position of the mirror in the image; the delineating module is based on the image RGB feature information, combined with depth information to adjust and determine the boundary of the mirror. This method is the first method that uses both RGB image and depth image to achieve mirror segmentation in an image. The present invention has also been further tested. For mirrors with a large area in a complex environment, the PDNet segmentation results are still excellent, and the results at the boundary of the mirrors are also satisfactory.
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公开(公告)号：US20220148292A1
公开(公告)日：2022-05-12
申请号：US17257704
申请日：2020-03-13
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin YANG , Xiaopeng WEI , Qiang ZHANG , Haiyang MEI , Yuanyuan LIU
IPC: G06V10/774 , G06V10/77 , G06V10/80 , G06V10/70 , G06V10/776 , G06V10/44
Abstract: The invention discloses a method for glass detection in a real scene, which belongs to the field of object detection. The present invention designs a combination method based on LCFI blocks to effectively integrate context features of different scales. Finally, multiple LCFI combination blocks are embedded into the glass detection network GDNet to obtain large-scale context features of different levels, thereby realize reliable and accurate glass detection in various scenarios. The glass detection network GDNet in the present invention can effectively predict the true area of glass in different scenes through this method of fusing context features of different scales, successfully detect glass with different sizes, and effectively handle with glass in different scenes. GDNet has strong adaptability to the various glass area sizes of the images in the glass detection dataset, and has the highest accuracy in the field of the same type of object detection.
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公开(公告)号：US20210216806A1
公开(公告)日：2021-07-15
申请号：US16963140
申请日：2020-05-13
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin YANG , Xiaopeng WEI , Qiang ZHANG , Yuhao LIU , Yu QIAO
Abstract: The invention belongs to the field of computer vision technology, and provides a fully automatic natural image matting method. For image matting of a single image, it is mainly composed of the extraction of high-level semantic features and low-level structural features, the filtering of pyramid features, the extraction of spatial structure information, and the late optimization of the discriminator network. The invention can generate accurate alpha matte without any auxiliary information, saving the time for scientific researchers to mark auxiliary information and the interaction time when users use it.
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公开(公告)号：US20240355140A1
公开(公告)日：2024-10-24
申请号：US18038611
申请日：2022-06-20
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin YANG , Xiaopeng WEI , Bo DONG , Haiwei ZHANG
CPC classification number: G06V40/20 , G06T7/20 , G06T7/70 , G06V10/764 , G06V10/7715 , G06V10/82 , G06V40/193 , G06T2207/30201
Abstract: The present invention belongs to the technical field of computer vision, and proposes a lightweight real-time emotion analysis method incorporating eye tracking. In the method, gray frames and event frames that have synchronized time are acquired through event-based cameras and respectively input to a frame branch and an event branch; the frame branch extracts spatial features by convolution operations, and the event branch extracts temporal features through conv-SNN blocks; the frame branch has a guide attention mechanism for the event branch; and the spatial features and the temporal features are integrated by fully connected layers, The final output is the average of the n fully connected layer outputs, which represents the final expression. The method can recognize the emotional expression of any stage in various complex light changing scenarios; and in the case of limited accuracy loss, the emotion recognition time is shortened to achieve “real-time” user emotion analysis.
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10.发明公开公开(公告)号：US20240295879A1
公开(公告)日：2024-09-05
申请号：US18464917
申请日：2023-09-11
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin YANG , Xuefeng YIN , Baocai YIN , Xiaopeng WEI
CPC classification number: G05D1/0274
Abstract: The present invention proposes an active scene mapping method based on constraint guidance and space optimization strategies, comprising a global planning stage and a local planning stage; in the global planning stage, the next exploration goal of a robot is calculated to guide the robot to explore a scene; and after the next exploration goal is determined, specific actions are generated according to the next exploration goal, the position of the robot and the constructed occupancy map in the local planning stage to drive the robot to go to a next exploration goal, and observation data is collected to update the information of the occupancy map. The present invention can effectively avoid long-distance round trips in the exploration process so that the robot can take account of information gain and movement loss in the exploration process, find a balance of exploration efficiency, and realize the improvement of active mapping efficiency.
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