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.

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 receives a claim from the customer. What they have to do is first, they have to check the damage to the vehicle. Without knowing how much damage is, companies can not know how much they have to claim.

How does an AI-based solution work in vehicle damage detection?

We can use the Mask R-CNN model to give better solutions to this problem. Mask R-CNN is an instance segmentation model that permits us to distinguish pixel-wise areas for our classes or labels. “instance segmentation” signifies individual sectioning items inside a scene, whether they are equivalent type — i.e., recognizing individual vehicles, people, and so forth.

The mask R-CNN is unique from the old type of object recognition models like Faster R-CNN. Notwithstanding recognizing the class and its bounding box area, it can also shade pixels with a color in the bounding box corresponding to that class.

Complete process

Data extraction

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 vehicle images. We can collect vehicle damaged images starter dataset using web scraping.

Data annotation

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.

Data processing

In the real world, images are full of noise. 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 the wrong patterns from noise images.

We are applying noise removal techniques to get denoised images. We also applied edge detection methods to find the exact damage part.

Model development

This is the perfect chance to assemble our model for our project. We can build a vehicle damage project using object detection methods since this is the kind of object detection based problem statement.

Object Detection

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).

The model will learn patterns to recognize damage and intensity of damage in this stage by utilizing these object detection algorithms.

Inference and deployment of the model

This step will apply the trained model on new images that are not available in training data; this dataset is called the test dataset. We can know how much a trained model will be performed on unseen images in the future from this step. 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.