Implementation of Mask-RCNN for detecting and segmenting damaged areas in car images for the purpose of damage assessment.

Car damage detection using Mask Regional Convolution Neural Network

Detectron2 for car damage detection using custom dataset

Custom YOLO11m model for detecting and classifying car body damage (99% shattered glass, 96% flat tire detection accuracy)—optimized for high-capacity inference and assistive use in inspection and service workflows like BMW pre-loaner inspections.

CNN model trained on a data set containing damage and normal car images and deployed as a web app using django.

Car Damage Detection: A computer vision project using YOLOv8 and Faster R-CNN to identify and localize car body defects like scratches, dents, and rust. Includes dataset creation, model training on Colab, comparison of results, and a user-friendly app for generating predictions.

No description available

Build a car damage detection model using Amazon Sagemaker and Tensorflow. Predict & mark “dent” and/or “scratch” in car images.

Computer Vision system for automatic car damage detection and assessment

AI methods for Car Damage Detection with Mask-RCNN

Car Damage Detection

Dent and Scratch detection on vehicles

It will detect the damage done to the car and classify into MINOR, MODERATE and SEVERE

Car damage detection using MaskRCNN

We're trying to make a full-featured automatic vehicle damage detection system.

2020 Financial-Technology Final Project

Use Facebook's Detectron to identify Car Damage

Car Damage Detection Using Mask RCNN

The repo includes two modules: Car damage detection using tensorflow model, Fraud Claims detection in insurance domain

No description available

A deep learning–based computer vision training pipeline for car damage detection using a Co-DETR learner enhanced with CBAM Attention, Hybrid Loss, and Albumentations. Trains on Colab to identify and localize car body defects such as scratches, dents, and rust. Includes end-to-end model training and quantitative evaluation.

Car damage detection from images using Detectron 2.

An AI-powered car damage detection system using YOLOv8 for automated vehicle damage assessment with comprehensive cost estimation and professional reporting.

car_damage detection using tensorflow object detection api

Web application of car damage detector

Automated car damage detection using Instance Segmentation(Mask R-CNN)

汽车损伤爆胎等识别图像分割系统源码＆数据集分享 [yolov8-seg-C2f-REPVGGOREPA等50+全套改进创新点发刊_一键训练教程_Web前端展示]

Development of a system of recognition of scratches in cars and preparing a detailed report about the scratches and other car details like make, model and license plate using Conventional Neural Networks. This Process is done by recording a video that splits the video into several data frames. Starting from the license plate at the rear end of the car and is recorded in a clockwise direction. The data frame consisting of the license plate is kept separately while the other data frames are used for identifying scratches .This Project implements Deep Learning Techniques. Pandas were used to split the recorded video into separate data frames.

An example project of tensorflow using tflite package in Flutter where you can check whether the car is damaged to not damage using phones camera.

It is a python code which is trained with a data set of damaged cars and it uses YOLO V3 model to detect the damages of the provided images.