Several industries have benefited from artificial intelligence's versatility and sophistication, and one new way this cutting-edge technology is being used is to identify vehicle damage. Making an automobile damage claim requires a lot of time.

Additionally, there is always a chance that claims leakage will occur, which is the discrepancy between quoted and actual claims settlement.

As a general rule, visual examination, quality analysis, and validation are required for claim approval. Processing the claims becomes more difficult as the assessment is delayed or is inaccurate. However, processing claims, validating findings, and inspections may all be done more quickly thanks to automated vehicle damage detection.

Vehicle Damage Detection: What Is It?

In the automobile industry, mishaps and minor vehicle damage are extremely prevalent. Problems, however, only appear after an insurance claim. The Government of Michigan's 2021 Fraud Investigation Unit Annual Report states that fraudulent auto injury claims added over $7.7 billion in extra payments to auto injury claims. Top auto insurers had annual premium leakage losses of up to $29 billion.

The automatic detection of a vehicle's outer body and evaluation of the extent of the damage are done using machine learning algorithms for automotive damage detection. Using computer vision and picture processing techniques, damage to the car is diagnosed not only for insurance purposes but also for estimating repair costs.

How to develop ML models that can identify car damage?

You require a diversified dataset of precisely labeled photos and videos in order to train ML models to recognize car damage. The machine learning model can't identify damages if the data isn't extremely accurate and accurately categorized. Data quality must be checked by human-in-the-loop annotators and annotation tools.

The models should be taught the following three parameters:

●determining whether there is harm
●Locating the damage on the vehicle by determining its precise location
●determining the extent of the damage based on its kind, location, and necessity for repairs

It is crucial to train the model to look for patterns and assess them after the vehicle damage assessment has been determined, categorized, and segmented. A machine learning algorithm that will evaluate and interpret the data should be applied to the training dataset.

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