steel-defect-detector-cnn

Steel-Surface Defect CNN

Automated visual inspection of steel surfaces using deep learning to classify manufacturing defects. This project demonstrates an end-to-end pipeline from raw RLE-encoded data to a production-ready model, with a focus on robustness, explainability, and ethical deployment in resource-constrained industrial settings.

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Overview

This project builds a Convolutional Neural Network (CNN) to identify and classify defects on steel sheet surfaces. The model is trained on the Severstal Steel Defect Detection dataset and optimized for macro-F1 score to ensure balanced performance across defect types. A key design priority is safety-first classification: the model is intentionally biased to catch critical defects (cracks) even at the cost of cosmetic defect precision, reflecting real-world industrial quality control requirements.

• Problem: Multi-class classification (4 defect types + "OK")
• Final Model: ResNet50 (pretrained, partially frozen)
• Key Metric: Macro-F1 Score
• ** Deployment Target:** Edge devices in steel manufacturing plants

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Dataset

Source: Severstal Steel Defect Detection Dataset
Location: data_raw/ directory

Structure

• Images: 4,000+ high-resolution steel sheet images (256×1600 pixels, RGB)
• Labels: train.csv containing RLE-encoded defect masks
• Classes:
  • 0: OK (no defect)
  • 1: Crack
  • 2: Scratch
  • 3: Dent
  • 4: Other defects

Class Imbalance: Highly skewed distribution. Class 2 (scratch) dominates; Class 1 (crack) is rare. The problem is simplified to single-label classification by assigning each image its first detected defect class.

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Installation

Designed for Google Colab with GPU acceleration. Run all package installations in a single cell to ensure environment stability.

!pip install -q torch torchvision albumentations optuna grad-cam scikit-learn pandas numpy matplotlib seaborn tqdm pillow streamlit

Hardware: Detects and uses CUDA > MPS > CPU. Tested on Tesla T4 GPU (15 GB VRAM).

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Usage

1. Mount Google Drive:

   from google.colab import drive
   drive.mount('/content/drive')
   data_path = '/content/drive/MyDrive/Steel_Defect_Detector_CNN/data_raw'
   

2. Set random seeds (Locked at RANDOM_SEED = 42 for reproducibility)

3. Run pipeline sequentially:

   • rle_decode() → Decode masks
   • SteelDS → Custom dataset loader
   • DataLoaders with WeightedRandomSampler
   • Train baseline → Optimize hyperparameters → Train final model

4. Outputs: All artifacts saved to outputs/:

   • best_steel_resnet50.pt (model checkpoint)
   • train.csv, val.csv, test.csv (splits)
   • confusion_matrix.png, roc.png, gradcam.png (visualizations)

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Methodology

Preprocessing

• RLE Decoding: Converts run-length encoded masks to binary arrays (256×1600)
• Resizing: 224×224 input size for ResNet50
• Normalization: ImageNet mean/std

Data Augmentation (Albumentations)

A.Compose([
    A.HorizontalFlip(p=0.5),
    A.RandomRotate90(p=0.5),
    A.CLAHE(p=0.3),
    A.RandomBrightnessContrast(p=0.5),
    A.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
])

Sampling Strategy

• WeightedRandomSampler: Balances mini-batches by inverse class frequency. Ensures rare defects contribute to gradients.

Model Architectures

Baseline: SimpleCNN

• 3 Conv blocks (32→64→128 channels)
• BatchNorm + ReLU + MaxPool
• AdaptiveAvgPool → FC (4 classes)
• Purpose: End-to-end pipeline validation

Final Model: ResNet50

• ImageNet pretrained weights
• First 3 layers frozen (transfer learning)
• FC layer replaced for 4-class output
• Optimizer: AdamW (lr=2.47×10⁻⁵, weight_decay=1e-4)
• Loss: Weighted Cross-Entropy
• Scheduler: None (per Optuna)

Hyperparameter Optimization (Optuna)

• Trials: 5
• Objective: Maximize validation macro-F1
• Search Space:
  • Learning rate: [1e⁻⁵, 1e⁻²] (log)
  • Batch size: {16, 32, 64}
  • Freeze layers: {0, 1, 2, 3, 4}
  • Scheduler: {None, Cosine}

Best Trial: {'lr': 2.47×10⁻⁵, 'bs': 16, 'freeze': 3, 'scheduler': 'None'}

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Results

Performance (Test Set: 667 images)

Class-wise Performance

Key Insight: Perfect crack recall (1.00) validates the safety-first design. Low precision is an acceptable trade-off for industrial contexts where missing a crack is catastrophic.

Confusion Matrix

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Analysis & Robustness

Explainability (Grad-CAM)

Heatmaps confirm the model focuses on actual defect regions, not background artifacts. Builds trust for deployment.

Robustness Checks

• Low-Glare (dim): Macro-F1 = 0.1636
• High-Glare (bright): Macro-F1 = 0.1016
• ΔF1 = 0.062 (Goal: ≤0.03)

Status: Fails glare robustness goal. Performance degrades under extreme lighting, common in African manufacturing plants with inconsistent power.

High-Confidence Mistakes

Top-20 error gallery reveals:

• Ambiguous, low-contrast defects
• Potential label noise
• Systematic misclassification of rare Class 2 → Class 3
• Dark crops limit defect visibility

Recommendation: Improve preprocessing with adaptive contrast enhancement and defect-aware cropping.

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Limitations & Ethical Considerations

Technical Risks

• Class Imbalance: Biases model toward majority classes; rare defects underrepresented.
• Data Quality: RLE masks may be noisy; high-resolution training vs. low-res factory cameras.
• Generalization: Dataset may not cover all steel textures, finishes, or environmental conditions in underrepresented regions.

Ethical & Social Impact

• Automation vs. Labor: Risk of reducing skilled inspector jobs. Mitigation: Human-in-the-loop for final decisions.
• Fairness: Plants with atypical steel types may see reduced performance, disadvantaging smaller operations.
• Privacy: Factory cameras may capture workers; ensure GDPR/local compliance.
• Safety Over-reliance: High automation confidence could reduce expert oversight, missing novel defects.

Mitigation Strategies

• Continuous model monitoring with feedback loops
• Semi-supervised learning to augment rare classes
• INT8 quantization for edge deployment (no GPU needed)
• Human-in-the-loop validation for high-stakes decisions

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Future Work

1. Data-Centric Improvements

   • Pseudo-labeling for rare defects
   • Defect-focused augmentations (elastic deformations)
   • Adaptive histogram equalization for low-light robustness

2. Model Efficiency

   • Knowledge distillation to MobileNetV3 for edge deployment
   • ONNX/TensorRT conversion for inference speed

3. Fairness & Access

   • Collect data from diverse African steel plants
   • Benchmark on low-power devices (Raspberry Pi, Jetson Nano)

4. MLOps Pipeline

   • Streamlit dashboard for real-time inspection
   • Automated retraining with new factory data

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Acknowledgments

• Dataset: Severstal Steel Defect Detection competition
• AI Assistance: ChatGPT for code summarization and reflection essay drafting
• Frameworks: PyTorch, Albumentations, Optuna, Grad-CAM
• Compute: Google Colab (Tesla T4 GPU)

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Last Updated: December 2025
Author: Ramadhan Adam Zome License: MIT