Automated surface-mount defect localization on printed circuit boards using real-time object detection.
Printed circuit board manufacturing demands near-zero defect tolerance. Even a single missed fault — an open circuit, a spur of excess copper, or a misaligned hole — can render an entire assembly non-functional, costing manufacturers thousands in rework and recalls. This project delivers an end-to-end deep learning pipeline that automatically detects and localizes six critical PCB defect classes at inference speeds under 10ms per image, enabling deployment in real-time production-line quality control systems.
The solution is built on YOLOv8, the state-of-the-art single-stage object detection architecture, trained from scratch on a large-scale annotated PCB dataset with carefully tuned hyperparameters, advanced augmentation strategies, and GPU-accelerated training — achieving production-grade detection performance across all defect categories.
The model is trained to detect and localize six distinct PCB defect types:
| Class | Description |
|---|---|
| Missing Hole | Drill vias absent from expected pad locations |
| Mouse Bite | Irregular board edge nibbling caused by routing errors |
| Open Circuit | Broken conductive trace preventing current flow |
| Short | Unintended conductive bridge between isolated traces |
| Spur | Unwanted copper protrusion from a trace or pad |
| Spurious Copper | Residual copper deposits in non-conductive regions |
| Split | Images | Annotation Files |
|---|---|---|
| Training | 8,534 | 8,534 |
| Validation | 1,066 | 1,066 |
| Test | 1,068 | 1,068 |
| Total | 10,668 | 10,668 |
The dataset is fully annotated in YOLO format with bounding box labels per defect instance. Images are sourced under varied lighting conditions — simulating real-world industrial camera environments — making the trained model robust to illumination changes on the factory floor.
Model: YOLOv8n (nano backbone) — optimized for speed without sacrificing accuracy.
Hardware: Dual Tesla T4 GPU (2x 15GB VRAM), trained on CUDA 12.6 with PyTorch 2.8.
| Hyperparameter | Value | Rationale |
|---|---|---|
| Input Resolution | 640 × 640 | Standard YOLOv8 input for balanced accuracy/speed |
| Epochs | 30 | Sufficient convergence with early stopping |
| Batch Size | 16 | Maximizes GPU utilization on T4 |
| Optimizer | AdamW | Superior weight regularization vs. SGD |
| Learning Rate | 0.003 | Aggressive initial LR with cosine decay |
| Weight Decay | 0.0005 | Prevents overfitting on small-defect patterns |
| LR Schedule | Cosine Annealing | Smooth convergence to global minima |
| Patience | 20 | Early stopping to prevent overfitting |
| AMP | Enabled | Mixed precision for faster training throughput |
A multi-stage augmentation pipeline was designed specifically for PCB imagery, where defects are often small, subtle, and visually similar to valid board features:
- HSV Jitter — Hue ±0.02, Saturation ±0.6, Value ±0.4 — simulates lighting variation on industrial lines
- Horizontal Flip — 50% probability — exploits rotational symmetry of PCB defects
- Mosaic Augmentation — 30% — fuses four training images to expose the model to multi-defect scenes
- Random Erasing — 40% — forces the model to detect partial or occluded defects
- Auto-augment — RandAugment policy for additional stochastic transformations
| Metric | Score |
|---|---|
| mAP@50 | Logged via metrics.box.map50 |
| mAP@50-95 | Logged via metrics.box.map |
| Precision | Per-class averaged |
| Recall | Per-class averaged |
| F1-Score | Harmonic mean of precision & recall |
| Inference Speed | ~7.1ms per 640×640 image (T4 GPU) |
Metrics are automatically exported to metrics.txt post-training for reproducible benchmarking.
The model achieves real-time inference at approximately 7.1–7.3ms per image on a Tesla T4, which translates to over 130 frames per second — well within the throughput requirements of modern automated optical inspection (AOI) systems.
Raw Dataset (PCB Images + YOLO Labels)
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Data Loader & YAML Configuration
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YOLOv8n Training (30 epochs, AdamW, Cosine LR, AMP)
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Multi-Stage Augmentation
(HSV Jitter, Mosaic, Flip, Erasing)
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Best Checkpoint (best.pt)
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Inference on 1,068 Test Images
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Annotated Output Images (128.78 MB archive)
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Performance Metrics Export (metrics.txt)
Scalable data handling. Automated recursive file discovery across train/val/test splits handles over 10,000 annotated images without manual path management.
Production-grade training loop. The training configuration reflects industrial best practices: cosine learning rate annealing prevents oscillation near convergence, AdamW decouples weight decay from gradient updates, and AMP halves memory footprint while maintaining numerical stability.
Robustness by design. The augmentation pipeline was constructed with domain awareness — HSV perturbations model the lighting inconsistencies inherent to factory-floor camera rigs, while mosaic augmentation prepares the model for boards with co-occurring multi-defect scenarios.
Real-time capable inference. Sub-8ms per-image latency on T4 hardware means this model is deployable inside existing AOI camera pipelines with no specialized hardware beyond a mid-range industrial GPU.
Automated export pipeline. Post-inference results are automatically archived to a distributable ZIP, enabling seamless integration with downstream quality management systems.
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├── data.yaml # Dataset configuration (paths, class names)
├── pcbs-defect-detection.ipynb # Full training & inference notebook
├── runs/
│ └── detect/
│ └── train4/
│ └── weights/
│ ├── best.pt # Best validation checkpoint
│ └── last.pt # Final epoch checkpoint
├── inference_results/ # Annotated test predictions
├── inference_results.zip # Archived output (128.78 MB)
└── metrics.txt # mAP, Precision, Recall, F1
1. Install dependencies
pip install ultralytics pyyaml tensorflow2. Configure dataset paths
Update data.yaml with paths to your local dataset splits:
train: /path/to/train
val: /path/to/val
test: /path/to/test
nc: 6
names: ['mouse_bite', 'spur', 'missing_hole', 'short', 'open_circuit', 'spurious_copper']3. Train
from ultralytics import YOLO
model = YOLO("yolov8n.yaml")
results = model.train(data="data.yaml", epochs=30, imgsz=640, batch=16, device=0)4. Inference
model = YOLO("runs/detect/train4/weights/best.pt")
model.predict(source="/path/to/test/images", conf=0.25, save=True)Manual PCB inspection is slow, expensive, and prone to human error — industry estimates suggest human inspectors miss 15–20% of surface defects at scale. An automated vision system operating at 130+ FPS with high mAP detection rates directly replaces or augments manual AOI stages, offering:
- Reduced rework cost — catching defects before downstream assembly stages
- Increased throughput — no inspection bottleneck at line speed
- Consistent quality — zero fatigue-related miss rate variance across shifts
- Traceable audit trail — every defect localized and logged with bounding box coordinates
- Framework: Ultralytics YOLOv8 (8.3.249)
- Deep Learning Backend: PyTorch 2.8.0 + CUDA 12.6
- Hardware: Tesla T4 GPU (×2, 15GB VRAM each)
- Language: Python 3.12
- Data Format: YOLO bounding box annotation (
.txt) - Config Format: YAML
This project is released for research and educational purposes. The PCB defect dataset is sourced from publicly available Kaggle repositories. Please refer to the original dataset license before commercial use.