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公开(公告)号：US20200027229A1
公开(公告)日：2020-01-23
申请号：US16514721
申请日：2019-07-17
Applicant: DeepScale, Inc.
Inventor： Anting Shen
Abstract: An annotation system uses annotations for a first set of sensor measurements from a first sensor to identify annotations for a second set of sensor measurements from a second sensor. The annotation system identifies reference annotations in the first set of sensor measurements that indicates a location of a characteristic object in the two-dimensional space. The annotation system determines a spatial region in the three-dimensional space of the second set of sensor measurements that corresponds to a portion of the scene represented in the annotation of the first set of sensor measurements. The annotation system determines annotations within the spatial region of the second set of sensor measurements that indicates a location of the characteristic object in the three-dimensional space.
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Patent Agency Ranking
公开(公告)号：US20180188733A1
公开(公告)日：2018-07-05
申请号：US15855749
申请日：2017-12-27
Applicant: DeepScale, Inc.
Inventor： Forrest Nelson Iandola , Donald Benton MacMillen , Anting Shen , Harsimran Singh Sidhu , Daniel Paden Tomasello , Rohan Nandkumar Phadte , Paras Jagdish Jain
CPC classification number: G05D1/024 , G05D1/0246 , G05D1/0255 , G05D1/0257 , G06F17/5009 , G06F17/5095 , G06N3/0454 , G06N3/084 , G06N20/00
Abstract: An autonomous control system combines sensor data from multiple sensors to simulate sensor data from high-capacity sensors. The sensor data contains information related to physical environments surrounding vehicles for autonomous guidance. For example, the sensor data may be in the form of images that visually capture scenes of the surrounding environment, geo-location of the vehicles, and the like. The autonomous control system simulates high-capacity sensor data of the physical environment from replacement sensors that may each have lower capacity than high-capacity sensors. The high-capacity sensor data may be simulated via one or more neural network models. The autonomous control system performs various detection and control algorithms on the simulated sensor data to guide the vehicle autonomously.
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公开(公告)号：US20200019799A1
公开(公告)日：2020-01-16
申请号：US16031965
申请日：2018-07-10
Applicant: DeepScale, Inc.
Inventor： Anting Shen , Forrest Nelson Iandola
Abstract: An annotation system provides various tools for facilitating training data annotation. The annotation tools include a bidirectional annotation model that generates annotations for an image sequence based on both forward information and backward information in an image sequence. The annotation system also facilitates annotation processes by automatically suggesting annotations to the human operator based on a set of annotation predictions and locations of interactions of the human operator on the image. This way, the annotation system provides an accelerated way to generate high-quality annotations that take into account input from a human operator by using the predictions as a guide when it appears that an estimated annotation is consistent with the judgement of the human operator. The annotation system also updates annotations for an overlapping set of objects based on input from human operators.
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公开(公告)号：US20180275658A1
公开(公告)日：2018-09-27
申请号：US15934899
申请日：2018-03-23
Applicant: DeepScale, Inc.
Inventor： Forrest Nelson Iandola , Donald Benton MacMillen , Anting Shen , Harsimran Singh Sidhu , Paras Jagdish Jain
Abstract: An autonomous control system generates synthetic data that reflect simulated environments. Specifically, the synthetic data is a representation of sensor data of the simulated environment from the perspective of one or more sensors. The system generates synthetic data by introducing one or more simulated modifications to sensor data captured by the sensors or by simulating the sensor data for a virtual environment. The autonomous control system uses the synthetic data to train computer models for various detection and control algorithms. In general, this allows autonomous control systems to augment training data to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment.
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公开(公告)号：US20200074304A1
公开(公告)日：2020-03-05
申请号：US16559483
申请日：2019-09-03
Applicant: DeepScale, Inc.
Inventor： Forrest Nelson Iandola , Harsimran Singh Sidhu , Yiqi Hou
Abstract: A neural network architecture is used that reduces the processing load of implementing the neural network. This network architecture may thus be used for reduced-bit processing devices. The architecture may limit the number of bits used for processing and reduce processing to prevent data overflow at individual calculations of the neural network. To implement this architecture, the number of bits used to represent inputs at levels of the network and the related filter masks may also be modified to ensure the number of bits of the output does not overflow the resulting capacity of the reduced-bit processor. To additionally reduce the load for such a network, the network may implement a “starconv” structure that permits the incorporation of nearby nodes in a layer to balance processing requirements and permit the network to learn from context of other nodes.
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公开(公告)号：US20200034710A1
公开(公告)日：2020-01-30
申请号：US16522411
申请日：2019-07-25
Applicant: DeepScale, Inc.
Inventor： Harsimran Singh Sidhu , Paras Jagdish Jain , Daniel Paden Tomasello , Forrest Nelson Iandola
Abstract: A model training and implementation pipeline trains models for individual embedded systems. The pipeline iterates through multiple models and estimates the performance of the models. During a model generation stage, the pipeline translates the description of the model together with the model parameters into an intermediate representation in a language that is compatible with a virtual machine. The intermediate representation is agnostic or independent to the configuration of the target platform. During a model performance estimation stage, the pipeline evaluates the performance of the models without training the models. Based on the analysis of the performance of the untrained models, a subset of models is selected. The selected models are then trained and the performance of the trained models are analyzed. Based on the analysis of the performance of the trained models, a single model is selected for deployment to the target platform.
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