AI Damage Detector Training Samples from Labeled Vehicle Images
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Solution Overview
Problem
Existing AI-based vehicle damage assessment systems face challenges in accurately detecting damages due to interference from additional information in input images, requiring sufficient positive and negative samples, and are prone to low accuracy, especially when not trained with similar damages.
Innovation Solution
A system that generates positive and negative image samples from labeled images using target bounding boxes and internal bounding boxes to train a target detector, allowing it to identify damages independently of the underlying vehicle parts by focusing on material-based damage detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used for automated damage detection, then processing time is reduced and labor costs are decreased, but detection accuracy deteriorates due to interference from additional information in input images
Solution Approach 1:
The patent extracts and removes additional information (background elements, other objects) from the input image to create a cleaned image that contains only the damaged vehicle. This extraction process eliminates interference from non-damage-related elements, allowing the AI model to focus solely on detecting damage characteristics, thereby maintaining high detection accuracy while preserving the automated processing speed benefit.
Solution Approach 2:
The patent segments the input image into relevant regions (damaged areas) and irrelevant regions (background, other objects). By dividing the image and selectively processing only the relevant segments for damage detection, the system reduces computational interference while maintaining processing efficiency. The segmentation allows the AI model to concentrate computational resources on damage-affected regions only.
2Measurement precision
If manual survey and damage assessment are performed by claim adjusters, then detection accuracy is maintained through expert evaluation, but processing time increases significantly and labor costs increase
Solution Approach 1:
The patent creates a synthesized copy of the damage assessment process using AI models trained on labeled images. Instead of requiring physical presence of claim adjusters, the system uses digital copies of damage data from labeled images to train the AI model. This copying approach enables automated replication of expert assessment logic, maintaining detection accuracy while eliminating the time and cost constraints of manual surveying.
Solution Approach 2:
The AI model performs self-service damage assessment by automatically analyzing input images without requiring human intervention. The system uses its own trained capabilities to detect, locate, and assess damage, replacing the need for claim adjusters to physically inspect vehicles. This self-service mechanism maintains assessment accuracy through continuous learning from labeled data while dramatically reducing processing time and labor requirements.
3Measurement precision
If sufficient positive and negative samples are generated for training, then detection accuracy is improved, but the complexity of sample generation and data processing increases
Solution Approach 1:
The patent introduces labeled images as an intermediary medium between raw input images and training data generation. These labeled images serve as a mediator that pre-identifies damage regions and provides ground truth annotations. By using labeled images as an intermediary, the system simplifies the sample generation process - instead of requiring complex algorithms to create training samples from scratch, the system leverages the pre-processed labeled data to efficiently generate both positive and negative samples for training.
Data Source
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AI summary
Embodiments described herein provide a system for facilitating image sampling for training a target detector. During operation, the system obtains a first image depicting a first target. Here, the continuous part of the first target in the first image is labeled and enclosed in a target bounding box. The system then generates a set of positive image samples from an area of the first image enclosed by the target bounding box. A respective positive image sample includes at least a part of the first target. The system can train the target detector with the set of positive image samples to detect a second target from a second image. The target detector can be an artificial intelligence (AI) model capable of detecting an object.