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When buying or renting an automobile, customers are more concerned with its visual appeal than with its material quality and fixtures. Quality control is required for car rentals and used car sellers. A rushed or inexperienced manual car inspection could cost these businesses money. Industrial image processing, sometimes known as "machine vision," is a key technology that is mostly employed in manufacturing lines. Real-time, efficient flaw detection systems are made possible by special cameras or imaging systems.

MBF Company has developed an affordable, user-friendly, portable automated vision inspection system that detects color and paint inconsistencies in automotive cars, allowing car rentals and dealers objectively analyze a vehicle at any time. A user-friendly device that combines durable hardware and newly created software is being developed. The portable device connects to a phone or a wearable device, such as smart glasses, and detects surface faults using Machine Vision and Machine Learning.

Defects can be identified by using a phone camera to capture reflected light patterns on a painted surface. On a painted surface, a machine learning and image processing system detects and locates faults and additional paint. A smartphone app provides the customer with immediate image feedback. The company's goal is to change the automotive industry by leveraging value for the user by doing objective paint and body inspections in a fraction of the time it takes a manual examination.

Features:

  • Applicability
  • Cost-effectiveness
  • Increased efficiency
  • Portability
  • High accuracy
  • No installation costs
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Considering the effectiveness of ScratchCatch in various ambient light circumstances, we decided to test our product on a rainy day on 4/27/2022. Obviously, no problems were discovered in the documentation of features. With the exception of photographs captured during bad weather, we encountered no issues with our product. The primary concern, however, is the usual dirtiness of automobile bodies on wet days, which could confound the artificial intelligence component of our solution. This issue could be resolved with operator verification. In conclusion, on a rainy day with an uncleaned automobile body, the findings of the AI component of our product may not be completely accurate and require operator confirmation, particularly around the car fender. In addition, it was strongly suggested to clean the car's exterior and work indoors.