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--- |
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license: gpl-3.0 |
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datasets: |
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- Msun/modelnet40 |
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language: |
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- en |
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metrics: |
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- accuracy |
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tags: |
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- deeplearning |
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--- |
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# **Automated Defect Detection in 3D Mesh Files Using Multi-Model Deep Learning Approaches** |
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## **📌 Project Overview** |
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This project introduces a **multi-modal deep learning approach** to detect **defects in 3D mesh files** by combining: |
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- **CNN (Convolutional Neural Network)** for **object classification** using **ModelNet40** dataset images. |
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- **GNN (Graph Neural Network)** for **defect identification** in **OFF files (3D mesh models).** |
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- **Fusion Model** integrating **CNN and GNN** for improved classification accuracy. |
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## **📁 Dataset & Novelty** |
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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. |
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### **🔹 Folder Structure** |
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``` |
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📦 Dataset |
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┣ 📂 Images |
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┃ ┣ 📂 train |
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┃ ┃ ┣ 📂 category_1 |
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┃ ┃ ┣ 📂 category_2 |
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┃ ┃ ┗ ... |
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┃ ┗ 📂 test |
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┗ 📂 OFF_files |
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┣ 📂 train |
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┃ ┣ 📂 category_1 |
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┃ ┃ ┣ 📂 normal |
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┃ ┃ ┗ 📂 defected |
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┃ ┣ 📂 category_2 |
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┃ ┃ ┣ 📂 normal |
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┃ ┃ ┗ 📂 defected |
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┗ 📂 test |
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``` |
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- **Images Folder** → Contains object images categorized into different classes (used for CNN). |
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- **OFF Files Folder** → Each category has **"normal"** and **"defected"** OFF files (used for GNN). |
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--- |
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## **🚀 Model Architecture** |
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### **🔹 CNN Model (Image Classification)** |
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- Uses a **pretrained CNN model (ResNet)** to classify objects. |
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### **🔹 GNN Model (Defect Identification)** |
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- Processes **OFF files** using **node features** and **adjacency matrices**. |
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- Uses a **13-layer deep GNN model** to capture mesh structure defects. |
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### **🔹 Multi-Modal Fusion Model** |
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- Combines **CNN and GNN outputs** using **fully connected layers**. |
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- Improves **accuracy by leveraging both image and graph information**. |
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--- |
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## **⚙️ Installation & Setup** |
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### **🔹 1️⃣ Install Dependencies** |
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```bash |
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pip install tensorflow numpy networkx trimesh |
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``` |
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### **🔹 2️⃣ Run Training** |
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```bash |
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python Utils/train.py |
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``` |
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### **🔹 3️⃣ Evaluate Model** |
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```bash |
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python Utils/evaluate.py |
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``` |
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--- |
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## **📊 Results & Evaluation** |
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- **CNN Classification Accuracy:** **76%** |
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- **GNN Defect Detection Accuracy:** **78%** |
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- **Fusion Model Accuracy:** **85%** |
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## **🛠️ Future Improvements** |
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- Use **a more complex GNN model** (with at least **13 layers**). |
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- Improve **multi-modal fusion model** by adding **extra layers**. |
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- Train on **a larger dataset** to improve generalization. |
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--- |
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## **👨💻 Author** |
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**Dhanush** |
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📧 Contact: [e-mail](mailto:[email protected]) |
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