VEHICLE-MOUNTED DEVICE AND METHOD FOR TRAINING OBJECT RECOGNITION MODEL

1 . 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.

2 . The method according to claim 1 , wherein the IOU=I/U, wherein “I” represents an area of an intersection area of the identified area of each object and the actual area of each object, and “U” represents an area of a union area of the identified area of each object and the actual area of each object.

3 . The method according to claim 1 , wherein the distance d=max(Δx/Lgt, Δy/Wgt), wherein “Δx” represents a difference between an abscissa of a first center point and an abscissa of a second center point, the first center point is a center point of the identified area of each object, and the second center point is a center point of the actual area of each object; “Δy” represents a difference between an ordinate of the first center point and an ordinate of the second center point; “Lgt” represents a length of the actual area of each object, and “Wgt” represents a width of the actual area of each object.

4 . The method according to claim 1 , wherein the 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 comprises:

when each of the IOU, the distance d, and the angle deviation value Δa associated with any one object falls within a corresponding preset value range, determining that the object recognition model correctly recognizes the any one object; and

when at least one of the IOU, the distance d, and the angle deviation value Δa associated with the any one object does not fall within the corresponding preset value range, determining that the object recognition model does not correctly recognize the any one object.

5 . The method according to claim 1 , wherein the neural network is a convolutional neural network.

6 . 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.

7 . The vehicle-mounted device according to claim 6 , wherein the IOU=I/U, wherein “I” represents an area of an intersection area of the identified area of each object and the actual area of each object, and “U” represents an area of a union area of the identified area of each object and the actual area of each object.

8 . The vehicle-mounted device according to claim 6 , wherein the distance d=max(Δx/Lgt, Δy/Wgt), wherein “Δx” represents a difference between an abscissa of a first center point and an abscissa of a second center point, the first center point is a center point of the identified area of each object, and the second center point is a center point of the actual area of each object; “Δy” represents a difference between an ordinate of the first center point and an ordinate of the second center point; “Lgt” represents a length of the actual area of each object, and “Wgt” represents a width of the actual area of each object.

9 . The vehicle-mounted device according to claim 6 , wherein the 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 comprises:

when each of the IOU, the distance d, and the angle deviation value Δa associated with any one object falls within a corresponding preset value range, determining that the object recognition model correctly recognizes the any one object; and

when at least one of the IOU, the distance d, and the angle deviation value Δa associated with the any one object does not fall within the corresponding preset value range, determining that the object recognition model does not correctly recognize the any one object.

10 . The vehicle-mounted device according to claim 6 , wherein the neural network is a convolutional neural network.