Abstract: Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop.

Abstract: An image processing system analyzes each of a set of vehicle images to determine if there is any damage to the vehicle depicted in each or any of the images. The image processing system uses a characterization engine in the form of a neural network based image model to process each of the pixels of each of the selected tagged images to determine the particular pixels of the image (or of the object depicted within the image) that depict the presence of damage to the object, and the likelihood of the pixels depicting damage. The characterization engine or image model may be developed or trained using a training engine that analyzes a plurality of images of different vehicles damaged in various different manners which have been annotated, on a pixel by pixel basis, to indicate which pixels of each image represent damaged areas of the objects and which have also been annotated, on an image basis, to indicate the view and/or zoom level of the image.

Abstract: An image processing system analyzes each of a set of vehicle images to determine one or more vehicle segments present in the images. The image processing system uses a characterization engine in the form of a neural network based image model to process each of the pixels of each of the images to determine the particular pixels of the image (or of the object depicted within the image) within or associated with a particular vehicle segment, such as a vehicle door, bumper, right or left panel, rear panel, windshield, etc. The characterization engine or image model may be developed or trained using a training engine that analyzes a plurality of images of different vehicles damaged in various different manners which have been annotated, on a pixel by pixel basis, to indicate the segments of the vehicle to which the various pixels belong or depict, and which have also been annotated, on an image basis, to indicate the view and/or zoom level of the image.

Abstract: Methods, systems, and techniques for utilizing image processing systems to measure damage to vehicles include utilizing an image processing system to generate a heat map of an image of a damaged vehicle, where the heat map is indicative of a damaged area of the vehicle, and determining at least one measurement of the damaged area based on the heat map and a depth of field indicator corresponding to the image. In some embodiments, the image processing system also determines one or more types of damage of the damaged area, and/or also generates a segmentation map of the depicted vehicle and utilizes the segmentation map in conjunction with the heat map to measure damaged areas and locations thereof on the vehicle depicted within the image. In some embodiments, the techniques include determining the depth of field indicator of the image or portions thereof.

Abstract: Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop.