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Vehicle Damage Detection

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What is the Objective of Vehicle Damage Detection?

Vehicle damage detection is the process of finding the damaged parts and the level of severity of those parts. In addition, we can know how much damage the vehicle got in the accident or anything else.

Why Do We Need Vehicle Damage Detection?

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In this paragraph, we will discuss what are the main key pain points facing every insurance company. Vehicle damage detection is most useful in insurance to claim money to clients. The insurance claim process typically involves five main stages, from getting a report call from a client to resolving their claim. For example, if a vehicle insurance agency receives a claim from the customer. After that, what first they have to do is, check the damage to the vehicle. However, without knowing how much damage is, companies can not know how much they have to claim. 

But insurance companies require experience people to estimate the severity of the vehicle damages. Moreover, they take more time to process the claim if they got more reports. In addition, clients may get irritating. So insurance companies need to resolve and accelerate this claim verification process. In the following section, we will discuss how we can improve this process by using Artificial Intelligence techniques.

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 individual sectioning 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 Process

    • Extracting Data From Various Sources

      Firstly, we have to extract the correct data for our problem statement. Data sets for vehicle damage detection are not publicly available. Insurance companies should already have a strategy to collect and organize the collection of vehicle images. However, we can collect vehicle damaged images for the starter dataset using web scraping.

    • Annotation Process

      Secondly, in the AI process, we have to annotate raw data with suitable tags. Suppose our AI model learns damaged parts patterns ultimately. In that case, 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 this paragraph, we will focus on the image data processing process. 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.

    • Model Development

      In addition to the above all data-related process in this paragraph we will discuss a little bit about the AI model. 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

      This model is using to decide the category and 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).

    • Inference and Deployment of the Model

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      In this paragraph, we focus on how we will apply the trained model to new images that are not available in training data in this step. From this step, we can know how well 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.

      In conclusion, Once we finalize our model, we can serve or host it using REST API. 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 ima



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