Image Quality Classification

Insurance
case study

Business Impacts

87%

Model Accuracy

<5s

Response Time

Customer Key Facts

  • Industry : Insurance
  • Country : US

Problem Context

The client, a US-based company provides a digital marketplace for Insurance Salvage vehicles. However, due to the bad image quality, customers withdrew their interest in buying vehicles, resulting in a potential loss in revenue. The client, therefore, required a solution that can assess image quality on the go and reject images failing to meet the standards.

Challenges

  • Access to the vehicle condition due to the bad quality of images
  • Various categories of image noise and different image angles results in many possibilities and complex solution
  • Inconsistency in image labeling for training the model

Technologies Used

Azure DevOps

Azure DevOps

Azure Machine Learning Service

Azure Machine Learning Service

Azure Repos </br><br>

Azure Repos

Azure Blob Storage

Azure Blob Storage

Developed a Computer Vision Solution Based on the Custom AI/ML Classification Model Technique

Solution

Quantiphi developed a computer vision solution based on the Custom AI/ML Classification Model technique. It identifies image noise such as blurs, glares, shadows, and so on in real-time, prompting field personnel to retake vehicle photos before presenting the car up for bid/sale.

Results

  • Developed an automated solution to improvise image quality and remove noise from images
  • Increased sales of vehicles as good quality images were submitted to customers
  • Ease of image categorization and end to end process in less than 5 seconds

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