Low-Quality Vehicle Damage Image Enhancement Using GANs
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Solution Overview
Problem
Conventional vehicle insurance claim settlement processes are inefficient due to the need for manual on-site inspections, leading to high labor costs and prolonged claim settlement periods, exacerbated by the use of low-quality vehicle damage photographs that hinder accurate damage identification.
Innovation Solution
A Generative Adversarial Network (GAN) based approach is employed to enhance the quality of vehicle damage images by training a quality discriminative model and a generative model, utilizing convolutional neural networks to improve image quality and reduce distortion, enabling more accurate loss assessment and identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual on-site inspection and loss assessment are performed by professional personnel, then accurate damage identification can be achieved, but labor costs increase and claim settlement period is prolonged
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated image processing system based on GANs and convolutional neural networks. The system automatically enhances image quality and performs loss assessment, eliminating the need for physical on-site inspection by personnel while maintaining or improving assessment accuracy and significantly reducing settlement time.
Solution Approach 2:
The patent introduces an image enhancement model as an intermediary between the captured low-quality images and the loss assessment algorithm. This intermediary component processes and enhances the images beforehand, enabling accurate automated assessment without requiring manual intervention, thus bridging the gap between automated processing and accurate damage identification.
2Productivity
If low-quality vehicle damage photographs are used, then the claim settlement process can be simplified and automated, but damage identification accuracy deteriorates
Solution Approach 1:
The patent performs image enhancement as a preliminary action before loss assessment. The image enhancement model processes the low-quality photographs first, improving their quality metrics (sharpness, brightness, contrast), and then the enhanced images are used for accurate automated loss assessment. This preliminary processing step enables both automation and accuracy.
Solution Approach 2:
The patent changes the quality parameters of the images through the enhancement model. The model adjusts parameters such as sharpness, brightness, and contrast to transform low-quality images into high-quality images suitable for accurate automated assessment, thereby maintaining productivity while improving measurement precision.
3Manufacturing precision
If image enhancement models are trained using high-quality images only, then the enhanced images achieve high quality, but the training process becomes complex and time-consuming
Solution Approach 1:
The patent applies local quality enhancement by focusing the training process on specific aspects of image quality that are most relevant for loss assessment. Rather than requiring all aspects of high image quality, the model is trained to enhance the specific features needed for accurate damage identification, reducing training complexity while maintaining necessary quality.
Solution Approach 2:
The patent incorporates feedback mechanisms in the training process where the enhancement model's output is evaluated and used to adjust training parameters. This feedback loop allows the model to learn from its own performance and progressively improve, reducing the need for manually curated high-quality training data and simplifying the overall training process.
Data Source
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AI summary
Embodiments of the present specification provide a method and apparatus for improving the quality of a vehicle damage image on the basis of a GAN network. The method comprises: acquiring a first image, the first image being a low-quality vehicle damage image; and inputting the first image into an image enhancement model to obtain a second image from an output of the image enhancement model, wherein the image enhancement model outputs the second image by improving the quality of the first image.