Deep learning-based system and method for automatically determining degree of damage to each area of vehicle
AssigneeAgilesoda Inc.
InventorsTae Youn Kim, Jin Sol Eo, Byung Sun Bae
Life of this patent
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Abstract
The present invention relates to a deep-learning based system and method of automatically determining a degree of damage to each area of a vehicle, which is capable of quickly calculating a consistent and reliable quote for vehicle repair by analyzing an image of a vehicle in an accident by using a deep learning-based Mark R-CNN framework and then extracting a component image corresponding to a damaged part, and automatically determining the degree of damage in the extracted component image based on a pre-trained model.
Claims8
A system for automatically determining a degree of damage for each vehicle area based on deep learning, the system comprising:
- a model generating unit which compares a plurality of vehicle photographed images obtained by photographing a state of the vehicle before an accident repair and a plurality of vehicle photographed images obtained by photographing a state of the vehicle after the accident repair, masks each component with a different color, learns subdivided data for the vehicle component for a bumper, a door, a fender, a trunk, and a hold based on the masked area, inspects and re-labels damage degree labelling data based on a result of a comparison between the damage degree labelling data for each damage type for the vehicle photographed image and a reference value, and learns data obtained by determining the degree of damage for each damage type of a plurality of damage area photographed images to generate a model;
- an image pre-processing unit which performs correction-processing on a vehicle photographed image obtained from a user terminal based on the model generated through the model generating unit;
- a component recognizing unit which recognizes and subdivides the correction-processed vehicle photographed image for each vehicle component based on the model generated through the model generating unit;
- a damaged area image extracting unit which extracts an image of a damaged vehicle component in the vehicle photographed image;
- and
- a damage degree determining unit which determines a degree of damage of the corresponding damaged area according to a predetermined state based on the extracted damaged area image and the model generated through the model generating unit.
A method of automatically determining a degree of damage for each vehicle area based on deep learning, the method comprising:
- comparing, by a model generating unit, a plurality of vehicle photographed images obtained by photographing a state of the vehicle before an accident repair and a plurality of vehicle photographed images obtained by photographing a state of the vehicle after the accident repair, masking each component with a different color, learning subdivided data for the vehicle component for a bumper, a door, a fender, a trunk, and a hold based on the masked area, inspecting and re-labelling damage degree labelling data based on a result of a comparison between the damage degree labelling data for each damage type for the vehicle photographed image and a reference value, and learning data obtained by determining the degree of damage for each damage type of a plurality of damage area photographed images to generate a model;
- performing, by an image pre-processing unit, correction-processing on a vehicle photographed image obtained from a user terminal based on the model generated through the model generating unit;
- recognizing and subdividing, by a component recognizing unit, the correction-processed vehicle photographed image for each vehicle component based on the model generated through the model generating unit;
- extracting, by a damaged area image extracting unit, an image of a damaged vehicle component in the vehicle photographed image;
- and
- determining, by a damage degree determining unit, a degree of damage of the corresponding damaged area according to a predetermined state based on the extracted damaged area image and the model generated through the model generating unit.
Dependencies are inferred from the claim text.