1. What is the objective of vehicle damage detection?
Vehicle damage detection is useful for detecting or finding the level of damage of a vehicle like a car, bike, truck, etc. we can know how much damage the car got in the accident or anything else.
2. Why do we need vehicle damage detection?
Vehicle damage detection is most useful in insurance to claim insurance money for clients. The insurance claim process typically involves five main stages, from the moment getting a call from a client of their report of loss to the resolution of their claim. For example, if a vehicle insurance agency gets a claim from the customer. What they have to do is, first they have to check the damage of the vehicle. Without knowing how much damage is, companies can not know how much they have to claim.
3. How does an AI-based solution work in vehicle damage detection?
We can use an object recognition model to give better solutions to this problem. The object recognition model should be an instance segmentation model that permits us to distinguish pixel-wise areas for our classes or labels. “instance segmentation” signifies sectioning individual items inside a scene, whether or not they are of the equivalent type — i.e, recognizing individual vehicles, people, and so forth.
AI-based Vehicle Damage Detection system
Data sets for automatic vehicle damage detection are not
publicly available. insurance companies involved in vehicle insurance should already have a strategy to collect and organize the collection of vehicle images. We can collect vehicle damaged images starter dataset using web scraping.
If our AI model learns damaged parts patterns ultimately, we should feed the annotated dataset to the models to find the damaged part. So in the annotation process, annotators will annotate damaged parts within the images by drawing bounding boxes around the broken pieces or damaged parts. If the annotation process is doing correctly, it means all damaged parts are perfectly tagged, and AI-Based models learn good patterns automatically to detect the level of damage. This annotation process will be useful to the model to understand correct patterns and improve model performance.
In the real world, images are fully noised. Because of camera quality, weather conditions we can’t take pictures without noise most of the time. Before building a model we should remove this from the image, otherwise, the model will learn wrong patterns from noise images.
We are applying noise removal techniques to get de-noised images. We also applied edge detection methods to find the exact damage part.
This is the perfect chance to assemble our model for our project. We can build a vehicle damage project by using object detection methods since this is the kind of object detection based problem statement.
Object Detection Model
Object Detection is utilizing to decide the category and area or location data of the object of interest for the image on the instance level. It very well may be used to discover precisely what kind of damage (e.g., scratches, dents, rust, broken) is found, at what location (bounding box data) and how extreme the damage is. The most famous object detection algorithms are RCNN (Region-based Convolutional Neural Networks), Fast RCNN, Faster RCNN, and SSD (Single Shot Detector).
By utilizing these object detection algorithms, the model will learn patterns to recognize damage and intensity of damage in this stage.
Inference and deployment of the model
In this step we will apply the trained model on new images that are not available in training data, this dataset is called the test dataset. From this step, we can know how good the trained model will be performed on unseen images in the future. If model performance is less as we expected then we can apply more techniques to increase performance. After getting better performance from our trained model we can use this model on real-world data.
Once we finalize our model we can serve or host it using REST API and by building a web application we can send requests to the REST API which gives us the damage detection and the severity of the damage of a particular vehicle damage image.