Ccc Intelligent Solutions Inc.

Mohan Liu

Neda Hantehzadeh

Steven Penny

Masatoshi Kato

Ranjini Vaidyanathan

Bhadresh Dhanani

Bahar Radfar

Srinivasan Krishnaswamy

Anneke Hidayat

Mina Haratiannezhadi

Sagar Bachwani

A tool used in an image processing system assists a user to train one or more statistical image models or classification engines (e.g., the CNN models) that are used to detect damaged areas on a vehicle, to detect damage types and/or to detect segments of the depiction of the vehicle. The tool enables a user to select and annotate various different training images to be used to train the models, wherein each of the training images depicts damage of one or more damage types to various different vehicles or automobiles (including automobiles of different years/makes/models). The tool displays each of a set of selected training images and enables a user to indicate, on the displayed selected training image, using an electronic pen, a touch screen or any other type of selector device, one or more sets of pixels within the displayed image that are associated with or that depict damage to the vehicle, one or more sets of pixels within the displayed image that are associated with or that depict a particular type of vehicle damage and/or one or more sets of pixels within the displayed image that are associated with or included in a particular segment of the depiction of the vehicle within the image. The marked or tagged images are then used to train an image model to detect vehicle damage, types of vehicle damage and/or vehicle segments in new vehicle images.

Tool for labelling training images for a vehicle characteristic detection model

An image processing system analyzes each of a set of vehicle images to determine one or more types of vehicle damage present in the selected 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 images to determine, for the particular pixels of the image (or of the object depicted within the image) at which damage exists, the type of damage that is present. The damage types may include a tear, a scratch, a dent, a missing piece, a misalignment or other types of 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 the type of vehicle damage that is present at each of the image pixels at which damage exists, and which have also been annotated, on an image basis, to indicate the view and/or zoom level of the image.

Image processing systems for detecting types of damage to a vehicle

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.

Methods and systems of utilizing image processing systems to measure objects

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Chicago, IL, United States of America

Mohan Liu