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US 2021/0201083 A1Published application
Vehicle-mounted device and method for training object recognition model
AssigneeMobile Drive Technology Co., Ltd.
InventorsKAI-CHUN WANG, Winston H. Hsu, TZU-KUEI HUANG, TING-HAO CHUNG, NAI-SHENG SYU, YU-CHING WANG, CHUN-HSIANG HUANG
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Abstract
A method of training an object recognition model includes obtaining a sample set. The sample set is divided into a training set and a verification set. The object recognition model is obtained by training a neural network using the training set, and the object recognition model is verified using the verification set.
Claims10
A method for training an object recognition model, the method comprising:
- collecting a predetermined number of point clouds ;
- obtaining a predetermined number of polar coordinates by converting cartesian coordinates of points of each point cloud of the predetermined number of point clouds to polar coordinates in a polar coordinate system;
- marking an actual area and an actual direction of each object corresponding to the polar coordinates of points of each point cloud;
- obtaining a predetermined number of samples by setting the polar coordinates of points of each point cloud as a sample, and setting the predetermined number of samples as a sample set;
- dividing the sample set into a training set and a verification set;
- obtaining the object recognition model by training a neural network using the training set, and verifying the object recognition model using the verification set;
- wherein the verifying the object recognition model using the verification set comprises:
- identifying an area and a direction of each object corresponding to each sample of the verification set using the object recognition model, such that an identified area and an identified direction of the each object corresponding to each sample are obtained;
- calculating an intersection over union (IOU) between the identified area of each object and the actual area of each object;
- calculating a distance d between the identified area of each object and the actual area of each object;
- and associating each object with the corresponding IOU and the corresponding distance d;
- calculating an angle deviation value Δa between the identified direction of each object and the actual direction of each object, and associating each object with the corresponding angle deviation value Δa;
- determining whether the object recognition model correctly recognizes each object according to the IOU, the distance d, and the angle deviation value Δa associated with each object;
- calculating an accuracy rate of the object recognition model based on a recognition result of the object recognition model recognizing each object corresponding to each sample of the verification set;
- and
- ending the training of the neural network when the accuracy rate of the object recognition model is greater than or equal to a preset value.
13 more elements
A vehicle-mounted device comprising:
- a storage device;
- at least one processor;
- and
- the storage device storing one or more programs, which when executed by the at least one processor, cause the at least one processor to:
- collect a predetermined number of point clouds;
- obtain a predetermined number of polar coordinates by converting cartesian coordinates of points of each point cloud of the predetermined number of point clouds to polar coordinates in a polar coordinate system;
- mark an actual area and an actual direction of each object corresponding to the polar coordinates of points of each point cloud;
- obtain a predetermined number of samples by setting the polar coordinates of points of each point cloud as a sample, and set the predetermined number of samples as a sample set;
- divide the sample set into a training set and a verification set;
- obtain an object recognition model by training a neural network using the training set, and verify the object recognition model using the verification set;
- wherein the verifying the object recognition model using the verification set comprises:
- identifying an area and a direction of each object corresponding to each sample of the verification set using the object recognition model, such that an identified area and an identified direction of the each object corresponding to each sample are obtained;
- calculating an intersection over union (IOU) between the identified area of each object and the actual area of each object;
- calculating a distance d between the identified area of each object and the actual area of each object;
- and associating each object with the corresponding IOU and the corresponding distance d;
- calculating an angle deviation value Δa between the identified direction of each object and an actual direction of each object, and associating each object with the corresponding angle deviation value Δa;
- determining whether the object recognition model correctly recognizes each object according to the IOU, the distance d, and the angle deviation value Δa associated with each object;
- calculating an accuracy rate of the object recognition model based on a recognition result of the object recognition model recognizing each object corresponding to each sample of the verification set;
- and
- ending the training of the neural network when the accuracy rate of the object recognition model is greater than or equal to a preset value.
17 more elements
Dependencies are inferred from the claim text.