Car Damage Detection

Instance segmentation of 7 types of physical damage on cars, for insurance, rental and resale inspection workflows. Given a photo, the model outputs a pixel mask, a bounding box, a damage class and a confidence score for every damaged region it finds.

More information: https://github.com/Naiscorp-Robotics/Car-Physical-Damage-AI

Examples

Five damaged regions across three classes

Tear and paint scratches

Side panel

Single crease, high confidence

A failure case — read this one. The dent on this hood is obvious to a human, but at the default threshold of 0.7 the model returns nothing. It only appears at 0.5, scoring 0.57:

The demo images do not cover all 7 classes — the only available example of Vỡ kính / Broken glass had a readable licence plate and a phone number burned into the frame, so it was excluded rather than published.

Installation

pip install cardamage

Quick start

import torch
from PIL import Image
from cardamage import AutoModel

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModel.from_pretrained("Naiscorp/car-damage-detection").to(device).eval()

overlay = model.inference(Image.open("car.jpg"))   # PIL.Image with masks drawn
overlay.save("damage.png")

Structured output instead of a picture:

r = model.predict(Image.open("car.jpg"))
r["boxes"]      # (N, 4) float32, xyxy in ORIGINAL image coordinates
r["scores"]     # (N,)   float32
r["classes"]    # (N,)   int64, 0..6
r["labels"]     # list[str], Vietnamese
r["labels_en"]  # list[str], English
r["masks"]      # (N, H, W) bool, pasted back to the original resolution

Labels

Training data

Using car-damage-dataset to train this model

License and attribution

model.py re-implements the inference algorithms of Detectron2 (Copyright 2019-present, Facebook, Inc. — licensed under the Apache License, Version 2.0), keeping the original module and parameter names so that checkpoints trained with Detectron2 load without any key remapping.