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公开(公告)号：US10783438B2
公开(公告)日：2020-09-22
申请号：US16442691
申请日：2019-06-17
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for on-device continual learning of a neural network which analyzes input data is provided to be used for smartphones, drones, vessels, or a military purpose. The method includes steps of: a learning device, (a) sampling new data to have a preset first volume, instructing an original data generator network, which has been learned, to repeat outputting synthetic previous data corresponding to a k-dimension random vector and previous data having been used for learning the original data generator network, such that the synthetic previous data has a second volume, and generating a batch for a current-learning; and (b) instructing the neural network to generate output information corresponding to the batch. The method can be performed by generative adversarial networks (GANs), online learning, and the like. Also, the present disclosure has effects of saving resources such as storage, preventing catastrophic forgetting, and securing privacy.
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公开(公告)号：US20200250986A1
公开(公告)日：2020-08-06
申请号：US16727041
申请日：2019-12-26
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for generating a lane departure warning (LDW) alarm by referring to information on a driving situation is provided to be used for ADAS, V2X or driver safety which are required to satisfy level 4 and level 5 of autonomous vehicles. The method includes steps of: a computing device instructing a LDW system (i) to collect information on the driving situation including information on whether a specific spot corresponding to a side mirror on a side of a lane, into which the driver desires to change, belongs to a virtual viewing frustum of the driver and (ii) to generate risk information on lane change by referring to the information on the driving situation; and instructing the LDW system to generate the LDW alarm by referring to the risk information. Thus, the LDW alarm can be provided to neighboring autonomous vehicles of level 4 and level 5.
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公开(公告)号：US10592799B1
公开(公告)日：2020-03-17
申请号：US16255012
申请日：2019-01-23
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： There is provided a method for determining an FL value to be used for optimizing hardware applicable to mobile devices, compact networks, and the like with high precision. The method includes steps of: a computing device (a) applying quantization operations to original values included in an original vector by referring to a BW value and each of FL candidate values, to thereby generate each of quantized vectors, including the quantized values, corresponding to each of the FL candidate values; (b) generating each of weighted quantization loss values, corresponding to each of the FL candidate values, by applying weighted quantization loss operations to information on each of differences between the original values and the quantized values included in each of the quantized vectors; and (c) determining the FL value among the FL candidate values by referring to the weighted quantization loss values and a device using the same.
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公开(公告)号：US10553118B1
公开(公告)日：2020-02-04
申请号：US16263220
申请日：2019-01-31
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for generating a lane departure warning (LDW) alarm by referring to information on a driving situation is provided to be used for ADAS, V2X or driver safety which are required to satisfy level 4 and level 5 of autonomous vehicles. The method includes steps of: a computing device instructing a LDW system (i) to collect information on the driving situation including information on whether a specific spot corresponding to a side mirror on a side of a lane, into which the driver desires to change, belongs to a virtual viewing frustum of the driver and (ii) to generate risk information on lane change by referring to the information on the driving situation; and instructing the LDW system to generate the LDW alarm by referring to the risk information. Thus, the LDW alarm can be provided to neighboring autonomous vehicles of level 4 and level 5.
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公开(公告)号：US10540572B1
公开(公告)日：2020-01-21
申请号：US16263393
申请日：2019-01-31
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
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：US10460210B1
公开(公告)日：2019-10-29
申请号：US16253996
申请日：2019-01-22
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
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：US10445611B1
公开(公告)日：2019-10-15
申请号：US16258186
申请日：2019-01-25
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyu Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for detecting at least one pseudo-3D bounding box based on a CNN capable of converting modes according to conditions of objects in an image is provided. The method includes steps of: a learning device (a) instructing a pooling layer to generate a pooled feature map corresponding to a 2D bounding box, and instructing a type-classifying layer to determine whether objects in the pooled feature map are truncated or non-truncated; (b) instructing FC layers to generate box pattern information corresponding to the pseudo-3D bounding box; (c) instructing classification layers to generate orientation class information on the objects, and regression layers to generate regression information on coordinates of the pseudo-3D bounding box; and (d) backpropagating class losses and regression losses generated from FC loss layers. Through the method, rendering of truncated objects can be performed while virtual driving, and this is useful for mobile devices and also for military purpose.
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公开(公告)号：US10410352B1
公开(公告)日：2019-09-10
申请号：US16257832
申请日：2019-01-25
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A learning method for improving a segmentation performance to be used for detecting events including a pedestrian event, a vehicle event, a falling event, and a fallen event using a learning device is provided. The method includes steps of: the learning device (a) instructing k convolutional layers to generate k encoded feature maps; (b) instructing k−1 deconvolutional layers to sequentially generate k−1 decoded feature maps, wherein the learning device instructs h mask layers to refer to h original decoded feature maps outputted from h deconvolutional layers corresponding thereto and h edge feature maps generated by extracting edge parts from the h original decoded feature maps; and (c) instructing h edge loss layers to generate h edge losses by referring to the edge parts and their corresponding GTs. Further, the method allows a degree of detecting traffic sign, landmark, road marker, and the like to be increased.
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公开(公告)号：US10410120B1
公开(公告)日：2019-09-10
申请号：US16258079
申请日：2019-01-25
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for learning an object detector based on a region-based convolutional neural network (R-CNN) capable of converting modes according to aspect ratios or scales of objects is provided. The aspect ratio and the scale of the objects including traffic lights may be determined according to characteristics, such as distance from the object detector, shapes, and the like, of the object. The method includes steps of: a learning device instructing an RPN to generate ROI candidates; instructing pooling layers to output feature vector; and learn the FC layers and the convolutional layer through backpropagation. In this method, pooling processes may be performed depending on real ratios and real sizes of the objects by using distance information and object information obtained through a radar, a lidar or other sensors. Also, the method can be used for surveillance as humans at a specific location have similar sizes.
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公开(公告)号：US10402978B1
公开(公告)日：2019-09-03
申请号：US16258156
申请日：2019-01-25
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for detecting a pseudo-3D bounding box based on a CNN capable of converting modes according to poses of detected objects using an instance segmentation is provided to be used for realistic rendering in virtual driving. Shade information of each of surfaces of the pseudo-3D bounding box can be reflected on the learning according to this method. The pseudo-3D bounding box may be obtained through a lidar or a rader, and the surface may be segmented by using a camera. The method includes steps of: a learning device instructing a pooling layer to apply pooling operations to a 2D bounding box region, thereby generating a pooled feature map, and instructing an FC layer to apply neural network operations thereto; instructing a convolutional layer to apply convolution operations to surface regions; and instructing a FC loss layer to generate class losses and regression losses.
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