-
公开(公告)号：US11915470B2
公开(公告)日：2024-02-27
申请号：US17746697
申请日：2022-05-17
发明人： Xian Wei , Jielong Guo , Chao Li , Hai Lan , Dongheng Shao , Xiaoliang Tang , Xuan Tang , Zhiyuan Feng
IPC分类号： G06V10/80 , G06V10/25 , G06V20/58 , G06V10/77 , G06V10/82 , G06V10/10 , G01S13/86 , G01S13/931
CPC分类号： G06V10/806 , G01S13/865 , G01S13/867 , G06V10/16 , G06V10/25 , G06V10/7715 , G06V10/82 , G06V20/58 , G01S13/931 , G06V2201/07
摘要： A target detection method based on fusion of vision, lidar and millimeter wave radar comprises: obtaining original data detected by a camera, a millimeter wave radar, and a lidar, and synchronizing the millimeter wave radar, the lidar, and the camera in time and space; performing a calculation on the original data detected by the millimeter wave radar according to a radar protocol; generating a region of interest by using a position, a speed, and a radar reflection area obtained from the calculation; extracting feature maps of a point cloud bird's-eye view and the original data detected by the camera; projecting the region of interest onto the feature maps of the point cloud bird's-eye view and the original data detected by the camera; fusing the feature maps of the point cloud bird's-eye view and the original data detected by the camera, and processing a fused image through a fully connected layer.
-
公开(公告)号：US11875576B2
公开(公告)日：2024-01-16
申请号：US18340090
申请日：2023-06-23
发明人： Jielong Guo , Xian Wei , Xuan Tang , Hui Yu , Jianfeng Zhang , Dongheng Shao , Xiaodi Yang , Yufang Xie
IPC分类号： G06V20/58 , G06V10/77 , G06V10/32 , G06V10/82 , G06V10/26 , G06V10/774 , G06V10/776 , G06V10/764
CPC分类号： G06V20/582 , G06V10/26 , G06V10/32 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/7715 , G06V10/82
摘要： Provided is a traffic sign recognition method based on a lightweight neural network, which including: a lightweight neural network model is constructed for training and pruning to obtain a lightweight neural network model; the lightweight neural network model comprises a convolution feature extraction part and a classifier part; the convolution feature extraction part includes one layer of conventional 3×3 convolution and 16 layers of separable asymmetric convolution. The classifier part includes three layers of separable full connection modules.
-
公开(公告)号：US20220277557A1
公开(公告)日：2022-09-01
申请号：US17746697
申请日：2022-05-17
发明人： Xian WEI , Jielong Guo , Chao Li , Hai Lan , Dongheng Shao , Xiaoliang Tang , Xuan Tang , Zhiyuan Feng
摘要： A target detection method based on fusion of vision, lidar and millimeter wave radar comprises: obtaining original data detected by a camera, a millimeter wave radar, and a lidar, and synchronizing the millimeter wave radar, the lidar, and the camera in time and space; performing a calculation on the original data detected by the millimeter wave radar according to a radar protocol; generating a region of interest by using a position, a speed, and a radar reflection area obtained from the calculation; extracting feature maps of a point cloud bird's-eye view and the original data detected by the camera; projecting the region of interest onto the feature maps of the point cloud bird's-eye view and the original data detected by the camera; fusing the feature maps of the point cloud bird's-eye view and the original data detected by the camera, and processing a fused image through a fully connected layer.
-
公开(公告)号：US20230334872A1
公开(公告)日：2023-10-19
申请号：US18340090
申请日：2023-06-23
发明人： Jielong GUO , Xian WEI , Xuan TANG , Hui YU , Jianfeng ZHANG , Dongheng SHAO , Xiaodi YANG , Yufang XIE
IPC分类号： G06V20/58 , G06V10/82 , G06V10/774 , G06V10/764 , G06V10/77 , G06V10/776 , G06V10/26 , G06V10/32
CPC分类号： G06V20/582 , G06V10/82 , G06V10/774 , G06V10/764 , G06V10/7715 , G06V10/776 , G06V10/26 , G06V10/32
摘要： Provided is a traffic sign recognition method based on a lightweight neural network, which including: a lightweight neural network model is constructed for training and pruning to obtain a lightweight neural network model; the lightweight neural network model comprises a convolution feature extraction part and a classifier part; the convolution feature extraction part includes one layer of conventional 3×3 convolution and 16 layers of separable asymmetric convolution. The classifier part includes three layers of separable full connection modules.
-
公开(公告)号：US20230274466A1
公开(公告)日：2023-08-31
申请号：US18313685
申请日：2023-05-08
发明人： Xian WEI , Jielong GUO , Hui YU , Xuan TANG , Hai LAN , Jianfeng ZHANG , Yufang XIE , Dongheng SHAO , Chao LI
CPC分类号： G06T9/001 , G06V10/7715 , G06V10/82
摘要： Disclosed are a point cloud polar coordinate coding method and a device, including dividing a circular scanning area scanned by a lidar at an equal angle with an angle Δθ to obtain a plurality of identical polar coordinate areas; dividing each of the polar coordinate areas with equal length along a radial direction with a length Δr to obtain a plurality of polar coordinate grids and generating a plurality of polar coordinate cylinders corresponding to each of the polar coordinate grids in a three-dimensional space; generating polar coordinate cylinder voxels; extracting structural features from the all point cloud data in each of the polar coordinate cylinder voxels; obtaining a two-dimensional point cloud pseudo-image; boundary supplementing to the two-dimensional point cloud pseudo-image; and performing feature extraction on the two-dimensional point cloud pseudo-image by using convolutional neural networks, and outputting a final feature map.
-
公开(公告)号：US11783037B1
公开(公告)日：2023-10-10
申请号：US18317512
申请日：2023-05-15
发明人： Jielong Guo , Xian Wei , Xuan Tang , Hui Yu , Dongheng Shao , Jianfeng Zhang , Jie Li , Yanhui Huang
CPC分类号： G06F21/566 , G06F21/552 , G06N3/08 , G06F2221/034
摘要： Disclosed are a defense method and a model of deep learning model aiming at adversarial attacks in the technical field of image recognition, which makes full use of the internal relationship between the adversarial samples and the initial samples, and transforms the adversarial samples into common samples by constructing a filter layer in front of the input layer of the deep learning model; the parameters of the filter layer are trained by using the adversarial attack samples, so as to improve the ability of the model to resist adversarial attack; then the trained filter layer is combined with the learning model after the adversarial training, and a deep learning model with strong robustness and high classification accuracy is obtained, which ensures that the recognition ability of the initial sample is not reduced while resisting the adversarial attacks.
-
-
-
-
-
搜索结果
6 条记录 (耗时 0.013 秒)
