---
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**  
```bash
pip install tensorflow numpy networkx trimesh
```

### **🔹 2️⃣ Run Training**  
```bash
python Utils/train.py
```

### **🔹 3️⃣ Evaluate Model**  
```bash
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](mailto:gdhanush270@gmail.com)

---