metadata

license: gpl-3.0
datasets:
  - Msun/modelnet40
language:
  - en
metrics:
  - accuracy
tags:
  - deeplearning

Automated Defect Detection in 3D Mesh Files Using Multi-Model Deep Learning Approaches

📌 Project Overview

This project introduces a multi-modal deep learning approach to detect defects in 3D mesh files by combining:

CNN (Convolutional Neural Network) for object classification using ModelNet40 dataset images.
GNN (Graph Neural Network) for defect identification in OFF files (3D mesh models).
Fusion Model integrating CNN and GNN for improved classification accuracy.

📁 Dataset & Novelty

The dataset used in this project is novel and proprietary, focusing on defect detection in 3D mesh files. Only the ModelNet40 dataset is publicly available.

🔹 Folder Structure

📦 Dataset
 ┣ 📂 Images
 ┃ ┣ 📂 train
 ┃ ┃ ┣ 📂 category_1
 ┃ ┃ ┣ 📂 category_2
 ┃ ┃ ┗ ...
 ┃ ┗ 📂 test
 ┗ 📂 OFF_files
   ┣ 📂 train
   ┃ ┣ 📂 category_1
   ┃ ┃ ┣ 📂 normal
   ┃ ┃ ┗ 📂 defected
   ┃ ┣ 📂 category_2
   ┃ ┃ ┣ 📂 normal
   ┃ ┃ ┗ 📂 defected
   ┗ 📂 test

Images Folder → Contains object images categorized into different classes (used for CNN).
OFF Files Folder → Each category has "normal" and "defected" OFF files (used for GNN).

🚀 Model Architecture

🔹 CNN Model (Image Classification)

Uses a pretrained CNN model (ResNet) to classify objects.

🔹 GNN Model (Defect Identification)

Processes OFF files using node features and adjacency matrices.
Uses a 13-layer deep GNN model to capture mesh structure defects.

🔹 Multi-Modal Fusion Model

Combines CNN and GNN outputs using fully connected layers.
Improves accuracy by leveraging both image and graph information.

⚙️ Installation & Setup

🔹 1️⃣ Install Dependencies

pip install tensorflow numpy networkx trimesh

🔹 2️⃣ Run Training

python Utils/train.py

🔹 3️⃣ Evaluate Model

python Utils/evaluate.py

📊 Results & Evaluation

CNN Classification Accuracy: 76%
GNN Defect Detection Accuracy: 78%
Fusion Model Accuracy: 85%

🛠️ Future Improvements

Use a more complex GNN model (with at least 13 layers).
Improve multi-modal fusion model by adding extra layers.
Train on a larger dataset to improve generalization.

👨‍💻 Author

Dhanush
📧 Contact: e-mail