Vehicle Damage Age Prediction Using Images and Usage Data
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Solution Overview
Problem
Manual vehicle damage assessment is inefficient, prone to human error, and can be deceived, leading to financial losses for insurance companies and vehicle owners, as it relies heavily on visual inspection and does not account for usage and external factors.
Innovation Solution
A system and method using image processing and machine learning classifiers to predict the true age of vehicle damages by analyzing images of damages and associated usage patterns, classifying damage types, materials, and intensities, and estimating remaining useful life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used to assess vehicle damages, then human judgment and flexibility are applied, but the assessment is prone to human error, deception, and inefficiency
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated digital system using image processing and machine learning classifiers. The system captures images of vehicle damages, processes them through computer vision algorithms, and uses trained classifiers to objectively determine damage age, type, and repair necessity, eliminating human subjectivity and error while improving inspection speed and efficiency
Solution Approach 2:
The system enables the damage assessment process to serve itself by using the captured images and vehicle usage data to automatically generate assessment results without requiring manual human intervention. The machine learning models self-evaluate the damage characteristics and produce reliable assessments independently
2Ease of operation
If visual inspection relies on look and feel, then simple and quick assessment is achieved, but the assessment can be easily deceived and is not foolproof
Solution Approach 1:
The patent transitions from two-dimensional visual inspection to multi-dimensional analysis by incorporating image processing data, vehicle usage patterns, environmental factors, and temporal information. The system analyzes multiple features including damage texture, color changes, oxidation levels, and correlates them with usage data to accurately determine damage age, moving beyond simple visual assessment to a comprehensive multi-parameter evaluation
3Reliability
If manual inspection is performed, then human expertise and contextual understanding are applied, but the process is time-consuming and not scalable
Solution Approach 1:
The system performs preliminary actions by capturing images and collecting vehicle usage data at the time of inspection, then uses pre-trained machine learning models to rapidly process this information. The classifiers have been trained in advance on extensive damage datasets, enabling them to quickly and accurately assess damage characteristics without requiring time-consuming manual analysis, thus reducing inspection time while maintaining high accuracy
Data Source
AI summary
A damage assessment method is provided. The method comprises recognizing a region of interest in an image that corresponds to a visible damage inflicted on a target object. The method further comprises determining a first plurality of feature values for a plurality of image features based on the recognized region of interest. The method further comprises retrieving, from a memory, time-series information that indicates a usage pattern of the target object and determining a second plurality of feature values for a plurality of usage features based on the retrieved time-series information. The method further comprises providing the first plurality of feature values and the second plurality of feature values as input to a trained classifier and predicting a true age of the visible damage based on a classification output of the trained classifier for the first plurality of feature values and the second plurality of feature values.