公开(公告)号：US11514555B2

公开(公告)日：2022-11-29

申请号：US17169534

申请日：2021-02-07

Applicant: NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS

Inventor： Jun Wang ,  Dening Lu ,  Dawei Li

IPC: G06T5/00 ,  G01B11/24 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure provides a point cloud denoising method based on deep learning for an aircraft part, in which different degrees of Gaussian noise are added based on a theoretical data model of the aircraft part, a heightmap for each point in the theoretical data model is generated, and a deep learning training set is constructed. A deep learning network is trained based on the constructed deep learning training set, to obtain a deep learning network model. A real aircraft part is scanned via a laser scanner to obtain measured point cloud data. The normal information of the measured point cloud is predicted based on the trained deep learning network model. Based on the predicted normal information, a position of each point in the measured point cloud data is further updated, thereby completing denoising of the measured point cloud data.

公开(公告)号：US11823425B2

公开(公告)日：2023-11-21

申请号：US18316326

申请日：2023-05-12

Applicant: Nanjing University of Aeronautics and Astronautics

Inventor： Jun Wang ,  Zhongde Shan ,  Shiyan Hua ,  Dawei Li

IPC: G06V10/774 ,  G06T7/00 ,  G06V10/764 ,  G06T7/73 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06T7/0004 ,  G06T7/73 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/82 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30164

Abstract: A few-shot defect detection method based on metric learning, including: (S1) performing data enhancement on a to-be-detected few-shot defect data set through a G2-Generative adversarial network (G2-GAN); (S2) extracting features of a defect data set similar to the to-be-detected few-shot defect data set based on an adaptive convolution kernel-based convolutional neural network (SKM-CNN) to generate a pre-training model; and (S3) transferring the pre-training model to a few-shot defect detection network (S2D2N) based on metric learning; and performing target feature extraction and metric learning in sequence to realize rapid identification and location of defects.

公开(公告)号：US11790517B2

公开(公告)日：2023-10-17

申请号：US18306166

申请日：2023-04-24

Applicant: Nanjing University of Aeronautics and Astronautics

Inventor： Jun Wang ,  Zhongde Shan ,  Shuyi Jia ,  Dawei Li ,  Yuxiang Wu

IPC: G06T7/00 ,  G06T7/73

CPC classification number: G06T7/0004 ,  G06T7/73 ,  G06T2207/20016 ,  G06T2207/20081

Abstract: A subtle defect detection method based on coarse-to-fine strategy, including: (S1) acquiring data of an image to be detected via a charge-coupled device (CCD) camera; (S2) constructing a defect area location network and preprocessing the image to be detected to initially determine a defect position; (S3) constructing a defect point detection network; and training the defect point detection network by using a defect segmentation loss function; and (S4) subjecting subtle defects in the image to be detected to quantitative extraction and segmentation via the defect point detection network.

公开(公告)号：US20230316736A1

公开(公告)日：2023-10-05

申请号：US17972942

申请日：2022-10-25

Applicant: Nanjing University of Aeronautics and Astronautics

Inventor： Jun Wang ,  Yuxiang Wu ,  Dawei Li ,  Yuan Zhang

IPC: G06V10/82 ,  G06V10/774 ,  G06V10/80

CPC classification number: G06V10/806 ,  G06V10/774 ,  G06V10/82

Abstract: The present disclosure disclose a method for feature detection of complex defects based on multimodal data, including feature extraction of multimodal data, multimodal feature cross-guided learning, multimodal feature fusion, and defect classification and regression. Feature extraction networks for multimodal two-dimensional data are constructed first, and a defect data set is sent to the networks for training; during training, cross-guided learning is implemented by using a multimodal feature cross-guidance network; then feature fusion is performed by using a weight adaptive method; and finally a defect detection task is implemented by using a classification subnetwork and a regression subnetwork. In the present disclosure, fusion of the multimodal data in a process of feature detection of the complex defects can be implemented efficiently, a capability of detecting the complex defects in an industrial environment can be improved more effectively, and production efficiency in an industrial manufacturing process is ensured.

公开(公告)号：US11359945B2

公开(公告)日：2022-06-14

申请号：US17169529

申请日：2021-02-07

Applicant: Nanjing University of Aeronautics and Astronautics

Inventor： Jun Wang ,  Dawei Li ,  Yuxiang Wu ,  Cheng Yi ,  Xu Xu

IPC: B61D15/12 ,  G01C7/06 ,  G01D21/02 ,  G01N21/88

Abstract: An inspection device for subway tunnel based on three-dimensional laser scanning includes a three-dimensional laser scanner, an adaptive structure of a track trolley, a power control module for the track trolley, a photoelectric sensor and a body of the track trolley. The power control module is arranged on the body. A support rod is vertically arranged on the power control module, and the three-dimensional laser scanner is mounted at a top of the support rod. The adaptive structure is symmetrically arranged at two sides of the body of the track trolley, and the photoelectric sensor is arranged in the body of the track trolley. The inspection device is designed to be modular, which is convenient to carry and repair, and easy to mount. In addition, the inspection device has low labor cost due to less manual intervention, and the inspection efficiency can be improved.