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The project has been executed in 2 methods one using Yolov5 for which u can see the demonstration in through the following link https://drive.google.com/file/d/1WTUw_j_NX_CqfcwwI7lozLnORTbWgHLp/view?usp=sharing and for the other approach using Detectron2 we have deployed it on hugging face

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title emoji colorFrom colorTo sdk sdk_version app_file pinned
Car Damage Detection
📉
purple
red
streamlit
1.10.0
app.py
false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Project Name : Vehicle Damage Detection

This project is for detecting the damage on car during accident. I have used YOLO v5 for model building and Streamlit for creating web app. You can upload both videos and images and check.

Dataset:

I have use pre annoted dataset from Roboflow for creating model. The dataset consists of 10675 images with 17 classes like Front-Windscreen-Damage, Headlight-Damage, Major-Rear-Bumper-Dent, Rear-windscreen-Damage,
RunningBoard-Dent, Sidemirror-Damage, Signlight-Damage, Taillight-Damage, bonnet-dent, doorouter-dent, fender-dent,
front-bumper-dent, medium-Bodypanel-Dent, pillar-dent, quaterpanel-dent, rear-bumper-dent, roof-dent.

Link for dataset:

https://universe.roboflow.com/cardamage/cardamage2-mrtqm/dataset/2

Installation of necessary pacakges.

pip install -r requirements.txt

Running streamlit

streamlit run damage_detect.py

Future Scope:

  1. I have used YoloV5s model. So further complex models can be created with more data.

Refernce:

https://github.com/xugaoxiang/yolov5-streamlit

https://github.com/ultralytics/yolov5

About

The project has been executed in 2 methods one using Yolov5 for which u can see the demonstration in through the following link https://drive.google.com/file/d/1WTUw_j_NX_CqfcwwI7lozLnORTbWgHLp/view?usp=sharing and for the other approach using Detectron2 we have deployed it on hugging face

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