Vehicle Damage Assessment Using Image Segmentation and Detection Models
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Solution Overview
Problem
Current vehicle damage assessment methods are inefficient and labor-intensive, relying on manual inspections by insurance company staff, which can lead to inconsistencies and delays in damage estimation and repair pricing.
Innovation Solution
A method and apparatus that combines image segmentation and detection results using a damage type determination mechanism, employing pre-trained models such as convolutional neural networks and region proposal networks to analyze vehicle damage images, segmenting and classifying damage types for improved assessment efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection by insurance company staff is used, then damage assessment can be performed with simple equipment, but the assessment efficiency is low and processing time is long
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated image processing system using convolutional neural networks and region proposal networks. The system automatically detects damage areas, segments damaged components, and generates assessment reports, eliminating the need for manual field inspections and significantly reducing processing time while maintaining assessment accuracy.
Solution Approach 2:
The system enables self-service damage assessment by automatically processing images uploaded by users through mobile applications or web interfaces. The automated models perform damage detection, component segmentation, and cost estimation without requiring insurance adjusters to physically inspect vehicles, allowing claims to be processed remotely and rapidly.
2Measurement precision
If manual damage assessment is performed, then equipment requirements are simple, but measurement precision and consistency vary among assessors
Solution Approach 1:
The patent applies segmentation by dividing the damage assessment task into distinct functional modules: damage area detection using convolutional neural networks, component identification using region proposal networks, and cost estimation. This modular architecture improves measurement precision for each specific task while organizing system complexity into manageable, independent components that can be trained and optimized separately.
Solution Approach 2:
The system uses parameter changes by adjusting model confidence thresholds, segmentation overlap parameters, and cost calculation weights to optimize assessment accuracy. The neural network models process images through multiple transformation stages, changing parameters such as feature extraction depth, region proposal scores, and damage severity classifications to achieve precise and consistent measurement results.
Data Source
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AI summary
A method and an apparatus for generating vehicle damage information are provided. The method includes: acquiring a damage area image of a target vehicle; performing image segmentation on the damage area image to obtain a first suspected damage area; inputting the damage area image to a pre-trained detection model to obtain a second suspected damage area, the detection model being configured to detect a location of the suspected damage area in the image; determining a damage image feature based on the first suspected damage area and the second suspected damage area; and inputting the damage image feature to a pre-trained classification model to generate a damage type, the classification model being configured to characterize a corresponding relationship between the image feature and the damage type. This embodiment provides a damage type determination mechanism combining comprehensive image segmentation results and image detection results, improving a vehicle damage assessment efficiency.