I’m always looking for use cases for AI and Machine Learning. I spoke with a good friend (Dane) about a use case with car inspections, his idea by the way! The problem for a car leasing company is that they had to perform a 10 point inspection of a car and log this information into backend systems. This included pictures of the car or sometimes just a manual visual inspection. Often if the car had a dent greater than a certain size they had the unfortunate job of telling the customer that they owed money to fix it in order to end the lease. This created a poor customer experience because they would complain that the car already had the dents before they leased the car. Additionally the information that was captured had to be keyed into backend systems which caused an additional administrative burden.




A better experience would be for the inspector to help the customer and find out how they can help them with their next car purchase instead of getting bogged down inspecting a car. The solution uses 4×360 cameras on the car and capture pictures before the car is inspected and after the car is returned. This data would then be processed by machine learning to recognize these dents without a human operator. Furthermore you could send these pictures to the customer so they could see the before and after. This helps to build trust with the customer.


To make this work a special area in the car leasing company would be created to pull in the car to capture the 360 picture. These would be high quality pictures that captures every part of the car. These would then be stitched together to form a 3d image that can be zoomed in/out and navigated. This process would be the same for the after picture as well. The question is why not just do this for the after picture to highlight only the damages pieces. The reason is you want to be transparent with the customer.


Once the pictures are taken the system applies a custom classifier machine learning model that detects dents on the car and provides an estimate of how much it will cost to fix this based upon historical data. The system then also uses Robotic Process Automation (Maybe AWS Robomaker, UIPath, etc.) to update legacy backend systems so the car leasing company doesn’t need to rip out these systems for this to work. The customer then gets a notification of the issues and potential options of how to handle this. They can see the before and after with a marked up images. This also reduces the follow up calls that the company gets from the customer.


Architecture (AWS)



Challenges to overcome. You need a decent image classification library based upon car and make and have separate models for each. My reasoning here is that each type of car and model will be so different. If you have Turk in the solution then you have a way of a human in loop type labelling operation. The recent “AWS Sagemaker Ground Truth” service from AWS could simplify this because it’s a single service that includes auto labeling and human in the loop. https://aws.amazon.com/sagemaker/groundtruth/


Other Applications for Solution:


  1. Insurance Claims. Use the solution to do after pictures : You could create an instant insurance payout amount and reduce costs of manual underwriting.
  2. Car Rental. When you return a car it performs this service : Benefits are that you can reallocate headcount.


Further reading: