Vehicle Damage Model Training for Reflection-Resistant Detection
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Solution Overview
Problem
Conventional vehicle insurance damage assessment methods are prone to inaccuracies due to interferences like reflections and dirt, leading to false positives and reduced accuracy in damage detection, and incur high labor and time costs.
Innovation Solution
A system that trains a damage identification model using tagged digital images, applying target detection techniques and reducing noise by identifying maximum damaged areas, to accurately determine damage categories and degrees independently of vehicle parts, utilizing convolutional neural networks and Gradient Boosted Decision Trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual survey and damage assessment are performed by adjusters, then damage identification can be conducted with human judgment, but labor costs and processing time increase significantly
Solution Approach 1:
The patent replaces the mechanical system of manual survey and assessment by human adjusters with an automated computer vision system. The system uses image processing algorithms and machine learning models to automatically detect damage, identify damaged parts, and estimate repair costs from photographs, eliminating the need for physical manual inspection while maintaining assessment accuracy.
Solution Approach 2:
The patent enables users to perform their own damage assessment by uploading photographs through a mobile application. The system processes these self-submitted images automatically, allowing policyholders to initiate and track their own claims without requiring an adjuster to physically visit the vehicle, thus significantly reducing processing time while maintaining reliability through automated analysis.
2Productivity
If automated image-based damage assessment is implemented, then processing time and labor costs are reduced, but accuracy decreases due to inter object reflection and environmental interferences
Solution Approach 1:
The patent segments the damage detection process into multiple specialized stages: image acquisition, pre-processing to remove reflections and dirt, damage region segmentation, part identification, and damage classification. By dividing the complex task into distinct processing stages, each optimized for specific challenges like reflection removal and edge detection, the system maintains high accuracy while achieving rapid automated processing.
Solution Approach 2:
The patent introduces intermediary processing steps between image capture and damage detection, including reflection removal algorithms, noise filtering, and enhancement techniques. These intermediary processes act as mediators that eliminate environmental interferences (reflections, dirt, lighting variations) before the main damage detection algorithms process the images, thereby preserving measurement precision in automated high-speed processing.
3Loss of information
If comprehensive damage assessment is performed manually, then detailed damage information can be obtained, but professional training costs and labor expenses increase
Solution Approach 1:
The patent implements a universal automated system that performs multiple functions: damage detection, damaged part identification, damage classification, and repair cost estimation. This multi-functional system replaces the need for specially trained human assessors by integrating various analysis capabilities into a single platform that can comprehensively evaluate damage while incurring no training costs and significantly reducing labor expenses.
Data Source
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AI summary
A system is provided for identifying damages of a vehicle. During operation, the system can obtain a set of digital images associated with a set of tagged digital images as training data. Each tagged digital image in the set of tagged digital images may include at least one damage object. The system can train a damage identification model based on the training data. When training the damage identification model, the system may identify at least a damage object in the training data based on a target detection technique. The system may also generate a set of feature vectors for the training data. The system can use the set of feature vectors to optimize a set of parameters associated with the damage identification model to obtain a trained damage identification model. The system can then apply the trained damage identification model to obtain a damage category prediction result.