Importance of Gold-standard training data to train Vehicle Damage Detection Model
Artificial Intelligence proves to be an instrumental technology in a number of enterprises adapted to support solutions for car damage. The early detection of car damage could benefit the auto insurance industry by reducing cost and improving customer satisfaction. Claiming car damage currently includes thorough review, often via human inspection of photos, resulting in inaccurate policy renewals.
What is Vehicle Damage Detection? Accidents are common in this industry, but when it comes time to file an insurance claim, that's when all the shenanigans start to happen. This article from Michigan State Police addresses the fraud and false claims that go with auto damage. The information might be helpful since there are billions of dollars being lost on both sides due to dishonest or fraudulent claims - you don't want to add your company to those numbers!
While the latest in driverless car design tends to be on everyone’s mind, there are many other ways in which self-driving technology can make a positive difference to our everyday lives that we haven't noticed. For example, one great application of this innovative engineering is the vehicle damage detection system.
This system obviously helps drivers with repair estimates, but it also has a lot more uses besides that. Among them is how this software could help prevent collisions (and even deaths) as well as making sure. Annotating the data sets is crucial here.
1. Object localization: The severity of the damage (along with its specific location) is detected and localized in a photograph by drawing bounding boxes around each damaged area. To speed up the process, this process can be combined with automated means for generating a separate bounding box for each individual damaged object in an image that presents multiple objects to help you pinpoint damage areas easily.
2. Challenges when Building Vehicle Damage Detection: It can be difficult to create a damage detection program for vehicles because there are reflections of light on the vehicle that might appear as damage, the environment should be varied, and one needs training datasets.
Acquiring high quality training data is easier said than done. In this case, the real-world images of vehicular damage contain some reflected images and metallic surfaces that might easily be mistaken for actual damage.
Having diverse images taken in multiple environments for examples will greatly help eliminate these extra reflections as well as represent a more complete range of relevant examples for the classifier to recognize with greater accuracy when performing classification on new imagery captured by the vehicle recording system.
3. Image pre-processing:
Vehicles could have exterior and interior surfaces that show wear and tear from long use. We would need to first create an albedo texture for these exterior and interior surfaces based on the intensity value of each pixel in the original image.
Once done, we can then add different other colors which will allow us to create a complete recollection of our vehicle's surface including doors, tires, locks, buttons, etc. To make it possible to separate the paneling of each of the colors with ease, we should perform segmentation techniques by using k-means algorithm or watersheds algorithms.
4. False Positives:
There's a high possibility that your car may be recalled or fixed because of false positives. It seems like according to the study done by Automotive News, recalls rose 40 percent due to automated diagnostics. This made people receive the false ideas that their cars were at risk for recall.
This creates a lot more pressure on their shoulders to take action and fix their vehicles immediately. We wanted to present a solution in which you can no longer receive worries about these problems because we understand that worrying can take its toll on our body causing us to feel sick from all the stress we've been storing up inside our bodies waiting for that day where we need to confront them.
Acgence provides high quality, accurate vehicle damage datasets that are tagged by a team of expert engineers and give you the tools necessary for modeling the highest quality Vehicle Damage Detection models possible! Acgence automotive-trained staff work closely with companies who may have specific needs in order to ensure that we can bring custom solutions to their business to help increase accuracy.