Systems and methods for automated data processing using machine learning for vehicle loss detection

A computer implemented method for processing digital images, the method comprising:

• receiving a plurality of possible images of a vehicle;
• applying an object detection machine learning model to the plurality of possible images, and based on the application of the object detection machine learning model to the plurality of possible images, determining, within each possible image, a location of the vehicle and defining a bounding box surrounding the location;
• performing operations that crop each said possible image to display only the vehicle and rotating each said possible image to a defined orientation for subsequent processing thereof;
• selecting, from the cropped and rotated possible images, a set of four distinct images of the vehicle, each of the set of four distinct images corresponding to a different defined angle view of the vehicle selected as being of interest in relation to a claim for the vehicle;
• generating a tiled image of the vehicle by combining the set of four distinct images into a single image concurrently displaying all four images of respective angle views in equal portions of the tiled image;
• inputting the tiled image to a first convolutional neural network, the first convolutional neural network trained based on historical tiled image data to extract a first set of image features from tiled images for predicting a first likelihood of total loss for the vehicle;
• inputting a multi-fusion set of images comprising the set of four distinct images individually into a second set of distinct and separate convolutional neural networks each configured and trained for separately receiving a different non-overlapping view of the vehicle and trained using historical multi-fusion images to extract a second set of image features from multi-fusion images, and fusing together the second set of images features for predicting, via a classifier, a second likelihood of total loss for the vehicle;
• receiving and inputting tabular data relating to the vehicle and the overall likelihood into a machine learning model, the machine learning model trained based on historical tabular data and associated features to predict a third likelihood of total loss for the vehicle;
• and
• aggregating the first, second and third likelihood of total loss vehicle to perform an average of a confidence score associated with each likelihood to determine an ensemble prediction of a classification of image and tabular data thereby determining the overall likelihood of whether the vehicle depicted in the set of images is repairable or total loss.

A computing device for processing digital images, the computing device comprising:

• a processor, a storage device and a communication device, wherein each of the storage device and the communication device is coupled to the processor, the storage device storing instructions, which when executed by the processor, configure the computing device to:
• receive a plurality of possible images of a vehicle;
• apply an object detection machine learning model to the plurality of possible images, and based on the application of the object detection machine learning model to the plurality of possible images, determine, within each possible image, a location of the vehicle and define a bounding box surrounding the location;
• perform operations that crop each said possible image to display only the vehicle and rotate each said possible image to a defined orientation for subsequent processing thereof;
• select, from the cropped and rotated possible images, a set of four distinct images of the vehicle, each of the set of four distinct images corresponding to a different defined angle view of the vehicle selected as being of interest in relation to a claim for the vehicle;
• generate a tiled image of the vehicle by combining the set of four distinct images into a single image concurrently displaying all four images of respective angle views in equal portions of the tiled image;
• input the tiled image to a first convolutional neural network, the first convolutional neural network trained based on historical tiled image data to extract a first set of image features from tiled images for predicting a first likelihood of total loss for the vehicle;
• input a multi-fusion set of images comprising the set of four distinct images individually into a second set of distinct and separate convolutional neural networks each configured and trained for separately receiving a different non-overlapping view of the vehicle and trained using historical multi-fusion images to extract a second set of image features from multi-fusion images, and fusing together the second set of images features for predicting, via a classifier, a second likelihood of total loss for the vehicle;
• receive and inputting tabular data relating to the vehicle and the overall likelihood into a machine learning model, the machine learning model trained based on historical tabular data and associated features to predict a third likelihood of total loss for the vehicle;
• and
• aggregate the first, second and third likelihood of total loss vehicle to perform an average of a confidence score associated with each likelihood to determine an ensemble prediction of a classification of image and tabular data thereby determining the overall likelihood of whether the vehicle depicted in the set of images is repairable or total loss.

A non-transitory computer-readable medium containing computer program code for processing digital images, the computer program code being executable by a processor for the processor to perform a method, the method comprising:

• receiving a plurality of possible images of a vehicle;
• applying an object detection machine learning model to the plurality of possible images, and based on the application of the object detection machine learning model to the plurality of possible images, determining, within each possible image, a location of the vehicle and define a bounding box surrounding the location;
• performing operations that crop each said possible image to display only the vehicle and rotating each said possible image to a defined orientation for subsequent processing thereof;
• selecting, from the cropped and rotated possible images, a set of four distinct images of the vehicle, each of the set of four distinct images corresponding to a different defined angle view of the vehicle selected as being of interest in relation to a claim for the vehicle;
• generating a tiled image of the vehicle by combining the set of four distinct images into a single image concurrently displaying all four images of respective angle views in equal portions of the tiled image;
• inputting the tiled image to a first convolutional neural network, the first convolutional neural network trained based on historical tiled image data to extract a first set of image features from tiled images for predicting a first likelihood of total loss for the vehicle;
• inputting a multi-fusion set of images comprising the set of four distinct images individually into a second set of distinct and separate convolutional neural networks each configured and trained for separately receiving a different non-overlapping view of the vehicle and trained using historical multi-fusion images to extract a second set of image features from multi-fusion images, and fusing together the second set of images features for predicting, via a classifier, a second likelihood of total loss for the vehicle;
• receiving and inputting tabular data relating to the vehicle and the overall likelihood into a machine learning model, the machine learning model trained based on historical tabular data and associated features to predict a third likelihood of total loss for the vehicle;
• and
• aggregating the first, second and third likelihood of total loss vehicle to perform an average of a confidence score associated with each likelihood to determine an ensemble prediction of a classification of image and tabular data thereby determining the overall likelihood of whether the vehicle depicted in the images is repairable or total loss.