Insurance companies perform a manual inspection for claim processing, which is neither scalable nor error-proof. In a usual scenario, an assessor would process images manually (even if they are taken with a digital camera), making the whole process lengthy and somewhat inefficient. Besides, the cost estimated by the car repair center is generally not genuine and rather exorbitant.
Srijan has proposed a solution that automates the initial assessment of the car damage by visually validating it and providing the approximate extent cost for the same, by categorizing them into the extent of the damage.
The proposed solution utilizes deep learning-based object detection and image classification methods to determine the damage status of a vehicle in percentage, for instance, whether the scratch, dent on the bumper, or headlamp damage is low, medium or high.
The tool takes the image or video of a damaged car as input and scrutinizes its sections to automatically find out the scratches, dents, rust, and breakages.
Try how the intelligent car damage detection tool works.
The technology stack used to build this solution is-
- Artificial Intelligence using Deep Learning
- Optical Character Recognition
- Digital Image Processing
- Machine Learning
Inspections are often the very first step of the car insurance claim process. The tool helps insurance companies in faster claim settlement of damaged cars to ensure greater customer satisfaction. In addition, it calculates the approximate cost involved in the repair, thereby saving a considerable amount of time and effort for the insurer.
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