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公开(公告)号：EP3474191A1
公开(公告)日：2019-04-24
申请号：EP18192810.2
申请日：2018-09-05
发明人： Kim, Yongjoong , Nam, Woonhyun , Boo, Sukhoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for constructing a table including information on a pooling type based on ranges of scale of at least one object in at least one image for constructing table is provided. The method includes steps of: generating a first pooled feature map by applying max pooling and a second pooled feature map by applying avg pooling; and acquiring a first candidate bounding box by using the first pooled feature map and acquiring a second candidate bounding box by using the second pooled feature map; and comparing a first degree of similarity between the first candidate bounding box and a GT bounding box with a second degree of similarity between the second candidate bounding box and the GT bounding box to thereby construct the table so as to include information on respective optimal pooling types by respective ranges of the scale of the object.
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公开(公告)号：EP3690731A3
公开(公告)日：2020-10-28
申请号：EP20153637.2
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for achieving better performance in an autonomous driving while saving computing powers, by using confidence scores representing a credibility of an object detection which is generated in parallel with an object detection process is provided. And the method includes steps of: (a) a computing device acquiring at least one circumstance image on surroundings of a subject vehicle, through at least one image sensor installed on the subject vehicle; (b) the computing device instructing a Convolutional Neural Network(CNN) to apply at least one CNN operation to the circumstance image, to thereby generate initial object information and initial confidence information on the circumstance image; and (c) the computing device generating final object information on the circumstance image by referring to the initial object information and the initial confidence information with a support of a Reinforcement Learning(RL) agent, and through V2X communications with at least part of surrounding objects.
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公开(公告)号：EP3690742A1
公开(公告)日：2020-08-05
申请号：EP20152011.1
申请日：2020-01-15
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for auto-labeling a training image to be used for learning a neural network is provided for achieving high precision. The method includes steps of: an auto-labeling device (a) instructing a meta ROI detection network to generate a feature map and to acquire n current meta ROIs, on the specific training image, grouped according to each of locations of each of the objects; and (b) generating n manipulated images by cropping regions, corresponding to the n current meta ROIs, on the specific training image, instructing an object detection network to output each of n labeled manipulated images having each of bounding boxes for each of the n manipulated images, and generating a labeled specific training image by merging the n labeled manipulated images. The method can be performed by using an online learning, a continual learning, a hyperparameter learning, and a reinforcement learning with policy gradient algorithms.
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公开(公告)号：EP3690725A1
公开(公告)日：2020-08-05
申请号：EP20152973.2
申请日：2020-01-21
申请人： Stradvision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A learning method for performing a seamless parameter switch by using a location-specific algorithm selection for an optimized autonomous driving is provided. And the method includes steps of: (a) a learning device instructing a K-th convolutional layer to apply a convolution operation to K-th training images, to thereby generate K-th feature maps; (b) the learning device instructing a K-th output layer to apply a K-th output operation to the K-th feature maps, to thereby generate K-th estimated autonomous driving source information; (c) the learning device instructing a K-th loss layer to generate a K-th loss by using the K-th estimated autonomous driving source information and its corresponding GT, and then to perform backpropagation by using the K-th loss, to thereby learn K-th parameters of the K-th CNN; and (d) the learning device storing the K-th CNN in a database after tagging K-th location information to the K-th CNN.
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公开(公告)号：EP3690716A1
公开(公告)日：2020-08-05
申请号：EP20152471.7
申请日：2020-01-17
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for merging object detection information detected by object detectors, each of which corresponds to each of cameras located nearby, by using V2X-based auto labeling and evaluation, wherein the object detectors detect objects in each of images generated from each of the cameras by image analysis based on deep learning is provided. The method includes steps of: if first to n-th object detection information are respectively acquired from a first to an n-th object detectors in a descending order of degrees of detection reliabilities, a merging device generating (k-1)-th object merging information by merging (k-2)-th objects and k-th objects through matching operations, and re-projecting the (k-1)-th object merging information onto an image, by increasing k from 3 to n. The method can be used for a collaborative driving or an HD map update through V2X-enabled applications, sensor fusion via multiple vehicles, and the like.
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公开(公告)号：EP3690714A1
公开(公告)日：2020-08-05
申请号：EP20151988.1
申请日：2020-01-15
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for acquiring a sample image for label-inspecting among auto-labeled images for learning a deep learning network, optimizing sampling processes for manual labeling, and reducing annotation costs is provided. The method includes steps of: a sample image acquiring device, generating a first and a second images, instructing convolutional layers to generate a first and a second feature maps, instructing pooling layers to generate a first and a second pooled feature maps, and generating concatenated feature maps; instructing a deep learning classifier to acquire the concatenated feature maps, to thereby generate class information; and calculating probabilities of abnormal class elements in an abnormal class group, determining whether the auto-labeled image is a difficult image, and selecting the auto-labeled image as the sample image for label-inspecting. Further, the method can be performed by using a robust algorithm with multiple transform pairs. By the method, hazardous situations are detected more accurately.
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公开(公告)号：EP3686806A1
公开(公告)日：2020-07-29
申请号：EP19207673.5
申请日：2019-11-07
申请人： Stradvision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A CNN-based method for meta learning, i.e., learning to learning, by using a learning device including convolutional layers capable of applying convolution operations to an image or its corresponding input feature maps to generate output feature maps, and residual networks capable of feed-forwarding the image or its corresponding input feature maps to next convolutional layer through bypassing the convolutional layers or its sub-convolutional layers is provided. The CNN-based method includes steps of: the learning device (a) selecting a specific residual network to be dropped out among the residual networks; (b) feeding the image into a transformed CNN where the specific residual network is dropped out, and outputting a CNN output; and (c) calculating losses by using the CNN output and its corresponding GT, and adjusting parameters of the transformed CNN. Further, the CNN-based method can be also applied to layer-wise dropout, stochastic ensemble, virtual driving, and the like.
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公开(公告)号：EP3686784A1
公开(公告)日：2020-07-29
申请号：EP19220210.9
申请日：2019-12-31
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method of neural network operations by using a grid generator is provided for converting modes according to classes of areas to satisfy level 4 of autonomous vehicles. The method includes steps of: (a) a computing device, if a test image is acquired, instructing a non-object detector to acquire non-object location information for testing and class information of the non-objects for testing by detecting the non-objects for testing on the test image; (b) the computing device instructing the grid generator to generate section information by referring to the non-object location information for testing; (c) the computing device instructing a neural network to determine parameters for testing; (d) the computing device instructing the neural network to apply the neural network operations to the test image by using each of the parameters for testing, to thereby generate one or more neural network outputs.
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公开(公告)号：EP3686783A1
公开(公告)日：2020-07-29
申请号：EP19220179.6
申请日：2019-12-31
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method of neural network operations by using a grid generator is provided for converting modes according to classes of areas to satisfy level 4 of autonomous vehicles. The method includes steps of: (a) a computing device instructing a pair detector to acquire information on locations and classes of pairs for testing by detecting the pairs for testing; (b) the computing device instructing the grid generator to generate section information by referring to the information on the locations of the pairs for testing; (c) the computing device instructing a neural network to determine parameters for testing by referring to parameters for training which have been learned by using information on pairs for training; and (d) the computing device instructing the neural network to apply the neural network operations to a test image by using each of the parameters for testing to thereby generate one or more neural network outputs.
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10.发明公开
公开(公告)号：EP3467774A1
公开(公告)日：2019-04-10
申请号：EP18192824.3
申请日：2018-09-05
申请人： StradVision, Inc.
发明人： Kim, Yongjoong , Nam, Woonhyun , Boo, Sukhoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for tracking a target object in frames of video data using Absorbing Markov Chain (AMC), including steps of: (a) acquiring a bounding box containing the target object in a current frame and a segmentation result for the target object in a previous frame; (b) obtaining obtain a region of interest (ROI) in the current frame by enlarging the bounding box to contain a portion of background information surrounding the target object; (c) acquiring information on local regions within the ROI in the current frame; (d) constructing an AMC graph using at least part of the local regions within the region of interest (ROI) in the current frame and local regions within a region of interest (ROI) in the previous frame; and (e) acquiring a segmentation result for the target object within the current frame by thresholding individual nodes in the AMC graph using absorption times thereof.
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