AUGMENTED PSEUDO-LABELING FOR OBJECT DETECTION LEARNING WITH UNLABELED IMAGES
A method includes obtaining an image of a scene and identifying one or more labels for one or more objects captured in the image. The method also includes generating one or more domain-specific augmented images by modifying the image, where the one or more domain-specific augmented images are associated with the one or more labels. In addition, the method includes training or retraining a machine learning model using the one or more domain-specific augmented images and the one or more labels. Generating the one or more domain-specific augmented images may include at least one of modifying the image to include a different amount of motion blur, modifying the image to include a different lighting condition, and modifying the image to include a different weather condition.
1 . A method comprising:
obtaining an image of a scene;
identifying one or more labels for one or more objects captured in the image;
generating one or more domain-specific augmented images by modifying the image, the one or more domain-specific augmented images associated with the one or more labels; and
training or retraining a machine learning model using the one or more domain-specific augmented images and the one or more labels.
2 . The method of claim 1 , wherein:
identifying the one or more labels comprises identifying the one or more labels using an initial machine learning model; and
training or retraining the machine learning model comprises retraining the initial machine learning model.
3 . The method of claim 1 , wherein generating the one or more domain-specific augmented images comprises at least one of:
modifying the image to include a different amount of motion blur;
modifying the image to include a different lighting condition; and
modifying the image to include a different weather condition.
4 . The method of claim 1 , wherein generating the one or more domain-specific augmented images comprises applying at least one geometric transformation to the image and to the one or more labels.
5 . The method of claim 1 , further comprising:
using the machine learning model to perform object detection.
6 . The method of claim 1 , wherein:
the image of the scene captures a scene around a vehicle; and
the one or more objects captured in the image comprise one or more objects around the vehicle.
7 . The method of claim 1 , wherein:
the image of the scene captures a scene within a vehicle; and
the one or more objects captured in the image comprise one or more portions of a driver's body.
8 . An apparatus comprising:
at least one processor configured to:
obtain an image of a scene;
identify one or more labels for one or more objects captured in the image;
generate one or more domain-specific augmented images by modifying the image, the one or more domain-specific augmented images associated with the one or more labels; and
train or retrain a machine learning model using the one or more domain-specific augmented images and the one or more labels.
9 . The apparatus of claim 8 , wherein:
the at least one processor is configured to identify the one or more labels using an initial machine learning model; and
the at least one processor is configured to train or retrain the initial machine learning model using the one or more domain-specific augmented images and the one or more labels.
10 . The apparatus of claim 8 , wherein, to generate the one or more domain-specific augmented images, the at least one processor is configured to at least one of:
modify the image to include a different amount of motion blur;
modify the image to include a different lighting condition; and
modify the image to include a different weather condition.
11 . The apparatus of claim 8 , wherein, to generate the one or more domain-specific augmented images, the at least one processor is configured to apply at least one geometric transformation to the image and to the one or more labels.
12 . The apparatus of claim 8 , wherein the at least one processor is further configured to use the machine learning model to perform object detection.
13 . The apparatus of claim 8 , wherein:
the image of the scene captures a scene around a vehicle; and
the one or more objects captured in the image comprise one or more objects around the vehicle.
14 . The apparatus of claim 8 , wherein:
the image of the scene captures a scene within a vehicle; and
the one or more objects captured in the image comprise one or more portions of a driver's body.
15 . A non-transitory machine-readable medium containing instructions that when executed cause at least one processor to:
obtain an image of a scene;
identify one or more labels for one or more objects captured in the image;
generate one or more domain-specific augmented images by modifying the image, the one or more domain-specific augmented images associated with the one or more labels; and
train or retrain a machine learning model using the one or more domain-specific augmented images and the one or more labels.
16 . The non-transitory machine-readable medium of claim 15 , wherein:
the instructions that when executed cause the at least one processor to identify the one or more labels comprise:
instructions that when executed cause the at least one processor to identify the one or more labels using an initial machine learning model; and
the instructions that when executed cause the at least one processor to train or retrain the machine learning model comprise:
instructions that when executed cause the at least one processor to retrain the initial machine learning model.
17 . The non-transitory machine-readable medium of claim 15 , wherein the instructions that when executed cause the at least one processor to generate the one or more domain-specific augmented images comprise:
instructions that when executed cause the at least one processor to at least one of:
modify the image to include a different amount of motion blur;
modify the image to include a different lighting condition; and
modify the image to include a different weather condition.
18 . The non-transitory machine-readable medium of claim 15 , wherein the instructions that when executed cause the at least one processor to generate the one or more domain-specific augmented images comprise:
instructions that when executed cause the at least one processor to apply at least one geometric transformation to the image and to the one or more labels.
19 . The non-transitory machine-readable medium of claim 15 , further containing instructions that when executed cause the at least one processor to use the machine learning model to perform object detection.
20 . The non-transitory machine-readable medium of claim 15 , wherein:
the image of the scene captures a scene around a vehicle; and
the one or more objects captured in the image comprise one or more objects around the vehicle.
21 . The non-transitory machine-readable medium of claim 15 , wherein:
the image of the scene captures a scene within a vehicle; and
the one or more objects captured in the image comprise one or more portions of a driver's body.