Systems and methods for automated data processing using machine learning for vehicle loss detection
A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.
1 . A computer system for processing a plurality of digital images, the computer system comprising:
a processor;
a non-transient computer-readable medium comprising instructions that, when executed by the processor, cause the processor to:
determine a location of a vehicle and define a bounding box that surrounds the location based on an object detection machine learning model being applied to distinct images of the vehicle;
crop the distinct images to display only the vehicle and rotate the distinct images to a defined orientation;
generate a tiled image of the vehicle the distinct images being combined into a single image of different angle views in equal portions of the tiled images;
process the generated tiled image via a first convolutional neural network configured to process images and trained based on historical tiled image data to extract a first set of image features from tiled images to predict a first likelihood of total loss for the vehicle; and
process via a second set of distinct and separate convolutional neural networks a multi-fusion set of images that comprise the set of distinct images, each of the second set of convolutional neural networks trained for a different non-overlapping view of the vehicle, with historical multi-fusion images, to extract a second set of image features from multi-fusion images to predict a second likelihood of total loss for the vehicle.
2 . The computer system of claim 1 , wherein the instructions, when executed, further cause the processor to rotate each cropped image to the defined orientation.
3 . The computer system of claim 1 , wherein the tiled image includes at least two cropped images combined into the single image.
4 . The computer system of claim 1 , wherein each of the images in the set of cropped images show the vehicle from a different view.
5 . The computer system of claim 4 , wherein the first convolutional neural network is applied to extract the first set of image features and the second set of distinct convolutional neural networks each trained for a particular view of the vehicle is applied as the respective machine learning model to extract the second set of image features.
6 . The computer system of claim 1 , wherein the different angle views are pre-defined.
7 . The computer system of claim 6 , wherein the instructions, when executed, further cause the processor to apply an image recognition machine learning model to classify cropped images as belonging to respective pre-defined views.
8 . The computer system of claim 1 , wherein the respective machine learning models process images with non-overlapping views of the vehicle.
9 . The computer system of claim 1 , wherein, to predict the likelihood of total loss, the instructions cause the processor to:
predict the first likelihood of total loss based on the first set of extracted features;
predict the second likelihood of total loss based on the second set of extracted features; and
predict the likelihood of total loss based on the first likelihood of total loss and the second likelihood of total loss.
10 . The computer system of claim 9 , wherein, to determine the second likelihood of total loss, the instructions cause the processor to fuse together the second set of extracted features.
11 . A computer implemented method for processing a plurality of digital images, the computer implemented method comprising:
determining a location of a vehicle and defining a bounding box surrounding the location based on applying an object detection machine learning model to distinct images of the vehicle;
cropping the distinct images to display only the vehicle and rotating the distinct images to a defined orientation;
generating a tiled image of the vehicle by combining the distinct images into a single image of different angle views in equal portions of the tiled image;
processing the generated tiled image via a first convolutional neural network configured for image processing and trained based on historical tiled image data to extract a first set of image features from tiled images to predict a first likelihood of total loss for the vehicle; and
processing via a second set of distinct and separate convolutional neural networks a multi-fusion set of images comprising the set of distinct images, each of the second set of convolutional neural networks trained for a different non-overlapping view of the vehicle, using historical multi-fusion images, to extract a second set of image features from multi-fusion images to predict a second likelihood of total loss for the vehicle.
12 . The computer implemented method of claim 11 , further comprising rotating each cropped image to the defined orientation.
13 . The computer implemented method of claim 11 , wherein the tiled image includes at least two cropped images combined into the single image.
14 . The computer implemented method of claim 11 , wherein each of the images in the set of cropped images show the vehicle from a different view.
15 . The computer implemented method of claim 14 , wherein the first convolutional neural network is applied to extract the first set of image features and the second set of distinct convolutional neural networks each trained for a particular view of the vehicle is applied as the respective machine learning model to extract the second set of image features.
16 . The computer implemented method of claim 11 , wherein the different angle views are pre-defined.
17 . The computer implemented method of claim 16 , further comprising applying an image recognition machine learning model to classify cropped images as belonging to respective pre-defined views.
18 . The computer implemented method of claim 11 , wherein the respective machine learning models process images with non-overlapping views of the vehicle.
19 . The computer implemented method of claim 11 , wherein predicting the likelihood of total loss further comprises:
predicting the first likelihood of total loss based on the first set of extracted features;
predicting the second likelihood of total loss based on the second set of extracted features; and
predicting the likelihood of total loss based on the first likelihood of total loss and the second likelihood of total loss.
20 . The computer implemented method of claim 19 , wherein, determining the second likelihood of total loss further comprises fusing together the second set of extracted features.
21 . A non-transitory computer-readable medium containing computer program code for processing a plurality of digital images, the computer program code being executable by a processor for the processor to perform a method, the method comprising:
determining a location of a vehicle and defining a bounding box surrounding the location based on applying an object detection machine learning model to distinct images of the vehicle;
cropping the distinct images to display only the vehicle and rotating the distinct images to a defined orientation;
generating a tiled image of the vehicle by combining the distinct images into a single image of different angle views in equal portions of the tiled image;
processing the generated tiled image via a first convolutional neural network configured for image processing and trained based on historical tiled image data to extract a first set of image features from tiled images to predict a first likelihood of total loss for the vehicle; and
processing via a second set of distinct and separate convolutional neural networks a multi-fusion set of images comprising the set of distinct images, each of the second set of convolutional neural networks trained for a different non-overlapping view of the vehicle, using historical multi-fusion images, to extract a second set of image features from multi-fusion images to predict a second likelihood of total loss for the vehicle.