An AI tool that streamlines your car insurance process by detecting damages with the most accuracy.
Watch the video demonstration: https://www.loom.com/share/c258aae54fcd494ab2c041cc9923b5f9 Car damages can be a major safety hazard if not detected and repaired in time. However, detecting damages in cars can be a tedious and time-consuming task for humans. This is where machine learning can be of great help. By training a machine learning model on a dataset of car images with damages, we can develop an automated system for detecting damages in cars. To install the necessary dependencies for this project, run the following command:pip install -r requirements.txtTo use the car damage detection model, follow these steps:
- Provide the path to the image you want to test
- The script will output whether the car has damages or not
from prediction_engine import get_yolov5
model = get_yolov5(input_image)
print(model)"/obj-to-json"This will return the predictions in a JSON format in the response body
{
"result": [
{
"xmin": 330.4482116699,
"ymin": 80.6117782593,
"xmax": 649.321105957,
"ymax": 470.5021972656,
"confidence": 0.9025965333,
"class": 15,
"name": "rear-bumper-dent"
}
]
}"/obj-to-img"This route will return the resulting image with bounding box around the detected damage area.
and finally,
"/severity"This route returns the severity of the damage into 3 classes, minor, moderate, severe.
{
"result": {
"severity": "minor"
}
}- Percentage value of damage on a vechicle
- Generalize the model for any damaged object
- Add a ChatGPT powered chatbot that assists the customer throughout the process
- Fire UI