Automating Vehicle Damage Detection using Deep Learning: A Beginner Guide

Introduction:

Vehicle accidents and damages are becoming increasingly common with the growing number of vehicles on the roads. Detecting vehicle damages manually can be a challenging and time-consuming task. However, with the recent advancements in deep learning and computer vision, it has become possible to automate the detection of vehicle damages. In this blog post, we will provide a complete guide on building a custom vehicle damage detector using deep learning techniques.

Why Vehicle Damage Detection is Important:

Vehicle damage detection is essential for several reasons. Firstly, it can help in assessing the severity of the damage and estimating the repair costs. Secondly, it can be used by insurance companies to speed up the claim settlement process based on user insurance claim images. Lastly, it can help to improve road safety by identifying potential hazards on the roads.

Challenges in Vehicle Damage Detection:

There are several challenges in vehicle damage detection. One of the main challenges is identifying the area of damage accurately, especially in images with complex backgrounds or lighting conditions. Another challenge is collecting and labeling sufficient data for training the model, which can be time-consuming and costly.

Current Methods of Vehicle Damage Detection:

Currently, vehicle damage detection is primarily done manually, which can be time-consuming and prone to human error. Some companies use computer vision techniques to assist in the detection of vehicle damages, but these techniques are often limited in their accuracy and require a lot of human intervention.

Automating Vehicle Damage Detection using Deep Learning:

Deep learning techniques can be used to automate the detection of vehicle damages. One such technique is the Mask R-CNN model, which extends the Faster R-CNN model by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The Mask R-CNN model is an instance segmentation model that allows identifying pixel-wise location for predefined classes, making it ideal for detecting vehicle damages.

Aim

To build a custom that can detect the area of damage on a vehicle.

Insurance Companies can use these models to speed up the claim settlement process based on the user insurance claim images.

Mask R-CNN

Fast R-CNN uses Selective Search on the layers of the CNN itself to identify Region Proposals. Faster R-CNN uses an object detection algorithm that allows the CNN itself to learn the Region Proposals.

Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is an instance segmentation model that allows identifying pixel wise location for predefined class. Mask R-CNN is different from classical object detection models like Faster R-CNN where, in addition to identifying the class and its bounding box location, it can also color pixels in the bounding box that correspond to that class. ![Mask R-CNN](media/Faster R-CNN.jfif)

1. Data Collection

The dataset is made up of 194 images (177 train + 11 validation + 6 test) from the internet.

Sample Image1:

Sample Car Damage 1

Sample Image 2:

Sample Car Damage 2

2. Data Annotation

The images were annotated using the roboflow and saved in the POV xml format. A Mask R-CNN model requires the images in the training dataset to be annotated, which is to have the region of damage in an image identified and the boundary of the damaged area marked accurately. Link to annotation tool https://app.roboflow.com. The annotations are then saved as a json format file in the dataset directory.

3. Import dataset and Visualization

Visualization of the mask on the annotated image is done in Object_Detection_Detecto_car.ipynb

4. Training model

The model is trained using the Detecto Library.

can run it directly from the Hugging Face Spaces: https://huggingface.co/spaces/lakshmi324/Vehicle_Damage_Detector

Installation

Clone this repository
Install dependencies
```
 pip3 install -r requirements.txt
```
Run setup from the repository root directory
```
 python3 setup.py install
```
Upload the vehicle image and check the output.

Conclusion:

In this blog post, we have introduced the concept of a vehicle damage detector and explained how it can be used to automatically detect and classify damage to vehicles from images. We have also discussed the data labelling process, the different available options for training a vehicle damage detection model, and the Detecto library which we have used to train our model.

What's Next:

There are several ways that we could improve the performance of our vehicle damage detector. One approach would be to collect and label more data, which would enable us to train a more accurate and robust model. We could also experiment with different neural network architectures and hyperparameters to try to improve the performance of our model. Additionally, we could explore the use of transfer learning to fine-tune pre-trained models for vehicle damage detection to idenitfy what type of damage whether it is bumper, glass broken etc.., which could help us to achieve better results with less data.